Event model training using in situ data

ABSTRACT

A method of identifying events within a wellbore comprises obtaining a first set of measurements of a first signal within a wellbore, identifying one or more events within the wellbore using the first set of measurements, obtaining a second set of measurements of a second signal within the wellbore, wherein the first signal and the second signal represent different physical measurements, training one or more event models using the second set of measurements and the identification of the one or more events as inputs, and using the one or more event models to identify at least one additional event within the wellbore.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to PCT/EP2021/065081filed Jun. 4, 2021, and entitled “Event Model Training Using In-SituData,” and PCT/EP2020/067045 filed on Jun. 18, 2020 and entitled “EventModel Training Using In-Situ Data,” each of which is incorporated hereinby reference in its entirety for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

It can be desirable to identify various events in a wellboreenvironment. For example, to obtain hydrocarbons from subterraneanformations, wellbores are drilled from the surface to access thehydrocarbon-bearing formation. After drilling a wellbore to the desireddepth, a production string is installed in the wellbore to produce thehydrocarbons from one or more production zones of the formation to thesurface. The production of the fluids can be detected at the wellheadbased on total flow of fluid. However, it can be difficult to determinewhere the fluid is inflowing into the wellbore when multiple productionszones are present and an extent of the fluid inflow (e.g., a fluidinflow rate).

BRIEF SUMMARY

In some embodiments, a method of identifying events within a wellborecomprises: obtaining a first set of measurements of a first signalwithin a wellbore; identifying one or more events within the wellboreusing the first set of measurements; obtaining a second set ofmeasurements of a second signal within the wellbore, wherein the firstsignal and the second signal represent different physical measurements;training one or more event models using the second set of measurementsand the identification of the one or more events as inputs; and usingthe one or more event models to identify at least one additional eventwithin the wellbore.

In some embodiments, a system for identifying events within a wellborecomprises: a memory; an identification program stored in the memory; anda processor, wherein the identification program, when executed on theprocessor, configures the process to: receive a first set ofmeasurements of a first signal within a wellbore; identify one or moreevents within the wellbore using the first set of measurements; receivea second set of measurements of a second signal within the wellbore,wherein the first signal and the second signal represent differentphysical measurements; train one or more event models using the secondset of measurements and the identification of the one or more events asinputs; and use the one or more event models to identify at least oneadditional event within the wellbore.

In some embodiments, a method of identifying events within a wellborecomprises: obtaining a first set of measurements of a first signalwithin a wellbore; identifying one or more events within the wellboreusing the first set of measurements, wherein the one or more eventscomprise a gas phase inflow, a liquid phase inflow, or sand ingress intothe wellbore; obtaining an acoustic data set from within the wellbore,wherein the first signal is not an acoustic signal; training one or morefluid inflow models using the acoustic data set and the identificationof the one or more events as inputs; and using the trained one or morefluid inflow models to identify at least one additional fluid inflowevent within the wellbore.

In some embodiments, a method of predicting wellbore sensor datacomprises: obtaining a first set of measurements of a first signalwithin a wellbore; identifying one or more events within the wellboreusing the first set of measurements; obtaining a second set ofmeasurements of a second signal within the wellbore, wherein the firstsignal and the second signal represent different physical measurements;training one or more event models using the second set of measurementsand the identification of the one or more events as inputs; identifying,using the one or more event models, one or more additional events withinthe wellbore; using the one or more additional events with one or moreformation properties; and predicting a third set of measurements inresponse to combining the one or more additional events with theformation properties, wherein the third set of measurements represents athird signal that is different than the first signal and the secondsignal.

In some embodiments, a system for predicting wellbore sensor datacomprises: a memory; a prediction program stored in the memory; and aprocessor, wherein the prediction program, when executed on theprocessor, configures the process to: receive a first set ofmeasurements of a first signal, wherein the first set of measurementsoriginate from within a wellbore; identify one or more events within thewellbore using the first set of measurements; receive a second set ofmeasurements of a second signal, wherein the second set of measurementsoriginate from within the wellbore, wherein the first signal and thesecond signal represent different physical measurements; train one ormore event models using the second set of measurements and theidentification of the one or more events as inputs; identify, using theone or more event models, one or more additional events within thewellbore; use the one or more additional events with one or moreformation properties; and determine a third set of measurements inresponse to combining the one or more additional events with theformation properties, wherein the third set of measurements representpredicted physical parameters within the wellbore, wherein the third setof measurements represents a third signal that is different than thefirst signal and the second signal.

In some embodiments, a method of predicting wellbore sensor datacomprises: training one or more event models using a second set ofmeasurements and an identification of one or more events as inputs,wherein a first set of measurements of a first signal are obtainedwithin a wellbore, wherein one or more events within the wellbore areidentified using the first set of measurements, wherein the second setof measurements of a second signal are obtained within the wellbore, andwherein the first signal and the second signal represent differentphysical measurements; identifying, using the one or more event models,one or more additional events within the wellbore; using the one or moreadditional events with one or more formation properties; and predictinga third set of measurements in response to combining the one or moreadditional events with the formation properties, wherein the third setof measurements represents a third signal that is different than thefirst signal and the second signal.

Embodiments described herein comprise a combination of features andcharacteristics intended to address various shortcomings associated withcertain prior devices, systems, and methods. The foregoing has outlinedrather broadly the features and technical characteristics of thedisclosed embodiments in order that the detailed description thatfollows may be better understood. The various characteristics andfeatures described above, as well as others, will be readily apparent tothose skilled in the art upon reading the following detaileddescription, and by referring to the accompanying drawings. It should beappreciated that the conception and the specific embodiments disclosedmay be readily utilized as a basis for modifying or designing otherstructures for carrying out the same purposes as the disclosedembodiments. It should also be realized that such equivalentconstructions do not depart from the spirit and scope of the principlesdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of various exemplary embodiments, referencewill now be made to the accompanying drawings in which:

FIG. 1 is a flow diagram of a method for identifying events within awellbore according to some embodiments;

FIG. 2A is a flow diagram of a method of identifying one or more eventswithin the wellbore using a first set of measurements;

FIG. 2B is a flow diagram of a method for identifying one or more fluidinflow locations within a wellbore using a first set of measurementsaccording to some embodiments;

FIG. 3 is a schematic, cross-sectional illustration of a downholewellbore environment according to some embodiments;

FIG. 4A and FIG. 4B are schematic, cross-sectional views of embodimentsof a well with a wellbore tubular having an optical fiber insertedtherein according to some embodiments;

FIG. 5 is a schematic view of an embodiment of a wellbore tubular withfluid inflow and sand ingress according to some embodiments;

FIG. 6 is a flow diagram of a method of determining fluid inflow ratesat one or more locations within a wellbore according to someembodiments;

FIG. 7 is a flow diagram of a method of predicting wellbore sensor dataaccording to some embodiments; and

FIG. 8 schematically illustrates a computer that may be used to carryout various methods according to some embodiments.

DETAILED DESCRIPTION

The following discussion is directed to various exemplary embodiments.However, one of ordinary skill in the art will understand that theexamples disclosed herein have broad application, and that thediscussion of any embodiment is meant only to be exemplary of thatembodiment, and not intended to suggest that the scope of thedisclosure, including the claims, is limited to that embodiment.

The drawing figures are not necessarily to scale. Certain features andcomponents herein may be shown exaggerated in scale or in somewhatschematic form and some details of conventional elements may not beshown in interest of clarity and conciseness.

Unless otherwise specified, any use of any form of the terms “connect,”“engage,” “couple,” “attach,” or any other term describing aninteraction between elements is not meant to limit the interaction todirect interaction between the elements and may also include indirectinteraction between the elements described. In the following discussionand in the claims, the terms “including” and “comprising” are used in anopen-ended fashion, and thus should be interpreted to mean “including,but not limited to . . . ”. Reference to up or down will be made forpurposes of description with “up,” “upper,” “upward,” “upstream,” or“above” meaning toward the surface of the wellbore and with “down,”“lower,” “downward,” “downstream,” or “below” meaning toward theterminal end of the well, regardless of the wellbore orientation.Reference to inner or outer will be made for purposes of descriptionwith “in,” “inner,” or “inward” meaning towards the central longitudinalaxis of the wellbore and/or wellbore tubular, and “out,” “outer,” or“outward” meaning towards the wellbore wall. As used herein, the term“longitudinal” or “longitudinally” refers to an axis substantiallyaligned with the central axis of the wellbore tubular, and “radial” or“radially” refer to a direction perpendicular to the longitudinal axis.The various characteristics mentioned above, as well as other featuresand characteristics described in more detail below, will be readilyapparent to those skilled in the art with the aid of this disclosureupon reading the following detailed description of the embodiments, andby referring to the accompanying drawings.

As utilized herein, a ‘fluid flow event’ includes fluid inflow (e.g.,any fluid inflow regardless of composition thereof), gas phase inflow,aqueous phase inflow, hydrocarbon phase inflow, any fluid outflow (e.g.,any fluid outflow regardless of composition thereof), gas phase outflow,aqueous phase outflow, hydrocarbon phase outflow, fluid flow within thewellbore (e.g., any fluid flow regardless of the composition thereof),any fluid injection, any fluid phase flow or mixed phase flow, and/orany fluid flow through one or more leak paths or annuli within thewellbore. The fluid can comprise other components such as solidparticulate matter (e.g., sand, etc.) in some embodiments, as discussedin more detail herein.

Disclosed herein are systems and methods for identifying events within awellbore, for example, determining the presence of an event, such as afluid flow event, sand ingress, a specific fluid phase, leaks, equipmentfailures, and the like at one or more locations within a subterraneanwellbore. As used herein, the term acoustic signals refers to signalsrepresentative of measurements of acoustic sounds, dynamic strain,vibrations, and the like, whether or not within the audible or auditoryrange.

Disclosed herein are systems and methods for identifying events within asubterranean wellbore, for example, so that a wellbore operator may moreeffectively control the fluid production from the wellbore. According toembodiments of this disclosure, an event can be identified within thewellbore, and data corresponding to the event can be obtained and usedto provide training data for one or more event models. The event can beidentified in a number of ways including inducing or having a known,local event, and/or using one or more sensors that are different fromthe obtained data to provide information to identify the event. Forexample, the injection of a fluid from an injection mandrel can be usedas a known fluid injection event at a specific location within thewellbore. Data from sensors within the wellbore during the fluidinjection can then be used to provide training data for a fluidinjection model. As another example, a first set of measurements or dataof a first signal within a wellbore can be utilized to provide identifyan event using one or more event models, and a second set ofmeasurements of a second signal within the wellbore can be obtainedbased on the event identification. The event identification using theone or more event models can be used with the second set of measurementsto provide training data for one or more additional event models.Utilizing the first set of measurements as a local reference to identifylocal events allows for the one or more event models to be trained usingidentified signals. This can help to provide data for training the oneor more models that might not otherwise be available, and/or providedata to allow one or more existing models to be calibrated. For example,the one or more event models may be trained using laboratory data andthen calibrated using the data obtained during an actual event.

By way of example, in some embodiments, the first set of measurementscomprises temperature features that can be determined from temperaturemeasurements taken along a length being monitored, such as a length of awellbore. The temperature measurements can be used in one or more firstwellbore event models that can provide an output indicative of eventlocation(s), for example, fluid inflow locations along a wellbore. Thiscan allow those locations with the event (e.g., fluid inflow) to beidentified using temperature based measurements (e.g., from thewellbore). When combined with a (e.g., distributed) temperature sensingsystem that can provide distributed and continuous temperaturemeasurements, the systems can allow for event (e.g., fluid inflow)locations to be tracked through time. In embodiments, various frequencydomain features can be obtained from an acoustic signal originating fromthe event (e.g., within the wellbore). The acoustic signals can beobtained using a distributed acoustic sensing (DAS) system that allowsfor continuous and distributed acoustic sensing. The acoustic signalscan be taken along the same portions of the length (e.g., length of thewellbore) as the temperature measurements, thereby allowing forinformation about the events (e.g., fluid inflow events), to bedetermined using both the temperature features and the frequency domainfeatures. The identification of the event using the temperaturemeasurements can be used to label the acoustic data, and correspondingfrequency domain features, to provide a frequency domain feature basedtraining set for one or more second event models. In some embodiments,one or more second event models can be trained with the one or moreevents identified via the DTS data and the acoustic measurements. Thetrained one or more second event models can subsequently be utilizedwith one or more frequency domain features to identify at least oneadditional event (e.g., fluid inflow locations and/or rates for one ormore fluids and/or fluid phases of a fluid inflow event).

In aspects, the one or more trained event models can subsequently beutilized alone or together with the one or more wellbore event models,thus allowing the event locations to be determined using temperaturefeatures, an/or acoustic features. The trained one or more second eventmodels can be used to verify or validate information (e.g., eventlocations) as determined from the one or more first or event modelsand/or other sensors. In aspects, the trained one or more event modelscan be utilized to predict sensor data. The herein disclosed systems andmethods can thus help to provide an improved event locationdetermination for use in managing the event.

FIG. 1 is a flow diagram of a method 10 for identifying events within awellbore according to some embodiments. As depicted in FIG. 1 , themethod 10 of identifying events within a wellbore comprises: identifyingone or more events within the wellbore at 13; obtaining a second set ofmeasurements of a second signal within the wellbore, at 15, wherein thefirst signal and the second signal represent different physicalmeasurements; training one or more event models using the second set ofmeasurements and the identification of the one or more events as inputs17; and using the one or more event models to identify at least oneadditional event at 19, which can be at least one additional event inthe same wellbore or a different wellbore. The one or more eventsidentified at step 13 can be identified using a local or induced event,and/or the one or more events can be identified based on a first set ofmeasurements of a first signal within the wellbore as an optionalprocess at step 11. The first set of measurements and/or the firstsignal can comprise signals from one or more sensors, and in someaspects, signals from multiple sensors can be used in the identificationof the event. When a first set of measurements obtained, theidentification of the one or more events can use the first set ofmeasurements and first signal to identify the one or more events withinthe wellbore at step 13.

The new signal processing architecture disclosed herein allows for theidentification of various events (e.g., the identification and detectionof the presence of the event at one or more locations) within awellbore. Such wellbore events can include a fluid inflow event (e.g.,including fluid inflow detection, fluid inflow location determination,fluid inflow quantification, fluid inflow discrimination, etc.), fluidoutflow event (e.g., fluid outflow detection, fluid outflowquantification), fluid flow within the wellbore (e.g., any fluid flowregardless of the composition thereof), any fluid injection, any fluidphase flow or mixed phase flow, and/or any fluid flow through one ormore leak paths or annuli within the wellbore, fluid phase segregation,fluid flow discrimination within a conduit, well integrity monitoring,including in-well leak detection (e.g., downhole casing and tubing leakdetection, leaking fluid phase identification, etc.), flow assurance(e.g., wax deposition), annular fluid flow diagnosis, overburdenmonitoring, fluid flow detection behind a casing, fluid inducedhydraulic fracture detection in the overburden (e.g., micro-seismicevents, etc.), sand detection (e.g., sand ingress, sand flows, etc.),and the like, each in real time or near real time in some embodiments.Without limitation, in embodiments, the one or more events identified at13 can comprise flow events, leak events, sand ingress events, leakevents, or a combination thereof.

In some aspects, the one or more events can be identified at step 13using known operating parameters such as an induced event. Sensor inputssuch as operating controls and sensors can be associated with the eventthat is known or controlled such that an identification of the event maybe known and/or one or more parameters of the event (e.g., an extent ofthe event) may be known. For example, a fluid can be injected into aknown location within the wellbore such that the identification of theevent (e.g., fluid injection) is occurring at a known location, and oneor more parameters of the event (e.g., fluid phase, flow rate, etc.) mayalso be known. This information can then be used as the identificationof the event that can be used with a second set of measurementassociated with the event to provide labeled data for training one ormore second event models.

As an example of a known event, a gas injection mandrel is a tool thatcan be disposed at a known depth in the wellbore and can be used toinject gas into the wellbore. The gas can serve to lift fluids withinthe wellbore towards the surface of the wellbore, thereby helping totransport the produced fluids along the wellbore. The flow rate throughthe gas injection mandrel can be determined and controlled using sensorsand controllers on the production site. Further, the composition of thegas can also be known based on the gas injection being induced andcontrolled as part of the gas injection process. In this example, theevent could include gas injection at a known location and/or gas flowalong the wellbore at a point above the gas injection mandrel. Theadditional parameters such as the gas flowrate would also be known basedon the sensors and controllers associated with the gas injectionprocess. Thus, a known and/or induced event can be used as the basis forcollecting additional sensor information associated with the eventand/or one or more event parameters.

In some aspects, the event may not be known, and a first set ofmeasurements can optionally be obtained that have a first signal used toidentify the one or more events at step 11. For example, fluid inflow,fluid leaks, overburden movements, sand ingress, and the like can occurwithin the wellbore. These events may often be transitory and theoccurrence of the event (e.g., an identification of the event), itsduration, and the extent of the event may not be easily known based oncontrollable operating parameters of the wellbore. In this instance, thefirst set of measurements comprising the first signal can be used withone or more first event models to identify the event. The first set ofmeasurements and/or the first signal can comprise signals from one ormore sensors, and in some aspects, signals from multiple sensors can beused in the identification of the event. The one or more first eventmodels can comprise any of the models as described herein, and can usethe first signal to identify the event, its duration, and/or extentwithin the wellbore. In some aspects, the one or more first event modelscan comprise models based on first principles analysis, physics-basedmodels, or the like. In some aspects, the one or more first event modelscan comprise one or more machine learning models. This can allow theevent and/or parameters associated with the event to be identified whenthe event is not known or induced.

When a first set of measurements is used as the basis for identifyingthe event within the wellbore, the first signal and the second signalcan be different. For example, the first signal and the second signalcan represent different physical measurements. Any type of signal usedin industrial processes can be used for the first signal and the secondsignal. In some aspects, the first set of measurements can comprise, forexample, at least one of an acoustic sensor measurement, a temperaturesensor measurement (e.g., distributed temperature sensor (DTS)measurements and/or point temperature sensor measurements), flow metermeasurements, pressure sensor measurements (e.g., distributed or pointpressure sensor measurements), a strain sensor measurements, positionsensor measurements, current meter measurements, level sensormeasurements, phase sensor measurements, composition sensormeasurements, optical sensor measurements, image sensor measurements, orany combination thereof. While the temperature and/or acousticmonitoring techniques described herein are indicated as beingdistributed measurements, any of the distributed measurements can alsobe achieved using one or more point sources, which can be individualsources or connected sources along a path.

In aspects, the second set of measurements obtained at 15 comprisesacoustic measurements obtained within the wellbore. Such acousticmeasurements can be obtained as described hereinbelow with reference toFIG. 3 , which is a schematic, cross-sectional illustration of adownhole wellbore environment 110 comprising wellbore 114 according tosome embodiments. As depicted in FIG. 3 , fiber optic distributedacoustic sensors (DAS) can be utilized to capture distributed acousticsignals, as described further hereinbelow.

As noted hereinabove, the first signal and the second signal representdifferent physical measurements. For example, in embodiments wherein thesecond set of measurements obtained at 15 comprise acoustic measurementsobtained within the wellbore 114, the first set of measurements will notcomprise such acoustic measurements or measurements of acoustic orvibrational waves (e.g., dynamic strain measurements). In aspects, thefirst set of measurements can comprise, for example, at least one ofdistributed temperature sensor (DTS) measurements, point temperaturemeasurements, production logging tool (PLT) measurements, flow metermeasurements, pressure sensor (e.g., distributed or point sensorpressure sensor) measurements, or combinations thereof. Fiber opticdistributed temperature sensors (DTS) can be utilized to capturedistributed temperature sensing signals, as described furtherhereinbelow. Although DTS is detailed hereinbelow, it is to beunderstood that a variety of combinations of first signal and secondsignal can be utilized to train one or more event models using thesecond set of measurements of the second signal and one or more eventsidentified using the first set of measurements of the first signal. Thatis, in embodiments, neither the first set of measurements nor the secondset of measurements comprises DTS measurements; in embodiments, neitherthe first set of measurements nor the second set of measurementscomprises DAS measurements; in embodiments, neither the first set ofmeasurements nor the second set of measurements comprises DTS or DASmeasurements.

As utilized herein, “fluid flow discrimination” indicates anidentification and/or assignment of the detected fluid flow (e.g.,single phase flow, mixed phase flows, time-based slugging, alteringfluid flows, etc.), gas flow, hydrocarbon liquid (e.g., ‘oil’) flow,and/or aqueous phase (e.g., water) flow, including any combined ormultiphase flows (e.g., inflows, outflows, and/or flows along thewellbore and/or one or more annuli). The methods of this disclosure canthus be utilized, in aspects, to provide information on various eventssuch as a fluid flow and/or a fluid flow point or location as well asflow regimes within a conduit rather than simply a location at whichgas, water, or hydrocarbon liquid is present in the wellbore tubular(e.g., present in a flowing fluid), which can occur at any point abovethe ingress or egress location as the fluid flows to or from the surfaceof the wellbore. In some embodiments, the system allows for aquantitative measurement of various fluid flows such as a relativeconcentration of in-well hydrocarbon liquid, water, and/or gas.

In some instances, the systems and methods can provide information inreal time or near real time. As used herein, the term “real time” refersto a time that takes into account various communication and latencydelays within a system, and can include actions taken within about tenseconds, within about thirty seconds, within about a minute, withinabout five minutes, or within about ten minutes of the action occurring.Various sensors (e.g., distributed temperature sensing sensors,distributed fiber optic acoustic sensors, point temperature sensors,point acoustic sensors, production logging tools, etc.) can be used toobtain a distributed temperature signal and/or an acoustic signal atvarious points along a length being monitored, for example, along awellbore. The distributed temperature sensing signal and/or the acousticsignal can then be processed using signal processing architecture withvarious feature extraction techniques (e.g., temperature featureextraction techniques, spectral feature extraction techniques) to obtaina measure of one or more temperature features, one or more frequencydomain features, and/or combinations thereof that enable selectivelyextracting the distributed temperature sensing signals and acousticsignals of interest from background noise and consequently aiding inimproving the accuracy of the identification of events, including, forexample, the movement of fluids (e.g., gas inflow locations, waterinflow locations, hydrocarbon liquid inflow locations, etc.) in realtime. While discussed in terms of being real time in some instances, thedata can also be analyzed at a later time at the same location and/or adisplaced location. For example, the data can be logged and lateranalyzed at the same or a different location.

As used herein, various frequency domain features can be obtained fromthe acoustic signal, and in some contexts, the frequency domain featurescan also be referred to herein as spectral features or spectraldescriptors. In some embodiments, the spectral features can compriseother features, including those in the time domain, various transforms(e.g., wavelets, Fourier transforms, etc.), and/or those derived fromportions of the acoustic signal or other sensor inputs. Such otherfeatures can be used on their own or in combination with one or morefrequency domain features, including in the development oftransformations of the features, as described in more detail herein.

In some embodiments, distributed temperature sensing signals andacoustic signal(s) can be obtained in a manner that allows for a signalto be obtained along a length of the sensor, for example, an entirewellbore or a portion of interest (e.g., a depth) thereof. In wellborecontexts, production logging systems can use a production logging system(PLS) to determine flow profile in wells. The PLS can be 10-20 meterslong and the sensors can be distributed along the length of the PLS. ThePLS can measure a variety of parameters such as temperatures, pressures,flow rates, phase measurements (e.g., gas flow rate, water flow rate,hydrocarbon flow rate, etc.), and the like. Furthermore, a PLS can berun through a well once or a few times (down and then up once or a fewtimes and out), and the sensors may be exposed to the conditions at agiven depth for a defined period of time (e.g., seconds to hours).Accordingly, PLSs can provide an indication that certain events, such asdownhole water inflow, may be occurring, on a time scale sufficient toidentify an event to allow a second set of measurements to be obtainedand used for training.

Fiber optic distributed temperature sensors (DTS) and fiber opticdistributed acoustic sensors (DAS) can capture distributed temperaturesensing and acoustic signals, respectively, resulting from downholeevents, such as wellbore events (e.g., gas flow, hydrocarbon liquidflow, water flow, mixed flow, leaks, overburden movement, and the like),as well as other background events. This allows for signal processingprocedures that distinguish events and flow signals from other sourcesto properly identify each type of event. This in turn results in a needfor a clearer understanding of the fingerprint of in-well event ofinterest (e.g., fluid flow, water flow, gas flow, hydrocarbon liquidflow, fluid flow along the tubulars, etc.) in order to be able tosegregate and identify a signal resulting from an event of interest fromother ambient background signals. As used herein, the resultingfingerprint of a particular event can also be referred to as an eventsignature, as described in more detail herein. In some embodiments,temperature features and acoustic features can each be used with a model(e.g., a machine learning model such as a multivariate model, neuralnetwork, etc.) to provide for detection, identification, and/ordetermination of the extents of various events. A number of differentmodels can be developed and used to determine when and where certainevents have occurred within a wellbore and/or the extents of suchevents.

The ability to identify various wellbore events may allow for variousactions or processes to be taken in response to the events. For example,reducing deferrals in wellbores resulting from one or more events suchas water ingress and facilitating effective remediation relies uponaccurate and timely decision support to inform the operator of theevents. As another example, with respect to events within a wellbore, awell can be shut in, production can be increased or decreased, and/orremedial measures can be taken in the wellbore, as appropriate based onthe identified event(s). An effective response, when needed, benefitsnot just from a binary yes/no output of an identification/detection ofin-well events but also from a measure of an extent of the event, suchas a relative amount of fluids (e.g., amount of gas flow, amount ofhydrocarbon liquid flow, amount of water flow, etc.) from each of theidentified zones of events so that zones contributing the greatest fluidamount(s) can be acted upon first to improve or optimize production. Thesystems and methods described herein can be used, in applications, toidentify the source of an event or problem, as well as additionalinformation about the event (referred to herein as an “extent” of theevent), such as a direction and amount of flow, and/or an identificationof the type of problem being faced. For example, when an eventcomprising water inflow and a location thereof are detected,determination of an extent of the inflow event comprising a relativeflow rate of the hydrocarbon liquid at the water inflow location mayallow for a determination of whether or not to remediate, the type ormethod of remediation, the timing for remediation, and/or deciding toalter (e.g., reduce) a production rate from the well. For example,production zones can be isolated, production assemblies can be open,closed, or choked at various levels, side wells can be drilled orisolated, and the like. Such determinations can be used to improve onthe drawdown of the well while reducing the production expensesassociated with various factors such as produced water.

Once obtained, the temperature and acoustic features can be used invarious models in order to be able to segregate a noise resulting froman event of interest from other ambient background noise. Specificmodels can be determined for each event by considering one or moretemperature features and/or acoustic features for known events. Thecombination of the temperature features and/or acoustic features with anidentification of the event and/or parameters associated with the eventcan be used to form a known data set used for training, which can bereferred to as a labeled data set. From these known events, thetemperature and/or acoustic features specific to each event can bedeveloped and signatures (e.g., having ranges or thresholds) and/ormodels can be established to determine a presence (or absence) of eachevent. Based on the specifics of each temperature and/or acousticfeature, the resulting signatures or models can be used to sufficientlydistinguish between events to allow for a relatively fast identificationof such events. The resulting signatures or models can then be usedalong with processed distributed temperature sensing and/or acousticsignal data to determine if an event is occurring at a point of interestalong the path of the temperature and/or acoustic sensor(s).

Any of the processing techniques disclosed herein can be used toinitially determine a signature or model(s), and then process andcompare the temperature and/or acoustic features in a sampledtemperature sensing and/or acoustic signal with the resulting signaturesor model(s). According to this disclosure, the events can be identifiedbased on being known or induced events, and/or identified using a firstset of measurements (e.g., DTS) of a first signal in the wellbore withone or more first event models. The identification of the event can thenbe used with a second set of measurements to provide labeled data thatcan be used to determine and/or train one or more second event modelsusing sensor data that is physically disparate from the first set ofmeasurements. In some aspects the determination and/or training of theone or more second event models can comprise using one or more knownsecond event models, and using the identified labeled data to calibratethe model, for example, by adjusting one or more parameters or aspectsof the model to match the in-situ data.

The systems and methods of this disclosure can be utilized for detecting(e.g., identifying one or more events out of many potential events) andcharacterizing wellbore events. In some embodiments, the wellbore eventscan comprise fluid flow locations and/or fluid flow regimes within aconduit in the wellbore. In some embodiments, other wellbore events suchas fluid phase segregation, fluid flow discrimination within a conduit,well integrity monitoring, in-well leak detection, annular fluid flowdiagnosis, overburden monitoring, fluid flow detection behind a casing,sand detection (e.g., sand ingress, sand flows, etc.), and the like canbe detected. In some aspects, the identification of the event(s) can bebased on using the sensor measurements at each location associated withthe sensor for a given sampling period, and multiple measurementsthrough time and/or along a length of the wellbore may not be needed inorder to identify one or more events from multiple possible events(e.g., the event identification need not be known prior to detecting thesignals).

As described herein, temperature features and/or spectral descriptors or‘frequency domain features’ can be used with DTS temperature and/or DASacoustic data processing, respectively, to provide for event detectionand/or event extent determination. For example, the temperature and/orspectral features can be used with wellbore eventdetection/identification (e.g., fluid profiling, fluid flow locationdetection, fluid phase discrimination such as the determination that thefluid at one or more locations such as the detected fluid flow locationcomprises gas flow, hydrocarbon liquid flow, aqueous phase flow, acombined fluid flow, and/or a time varying fluid flow such as sluggingsingle or multiphase flow, and the like). One or more first or wellboreevent models can be utilized herein for event identification. Onceidentified, the event identification along with a second set ofmeasurements from a second sensor (e.g., labeled data) can subsequentlybe utilized to train one or more second event models. Once trained, theone or more second event models can be utilized alone or in combinationwith one or more wellbore event models or other sensor data to identifyat least one additional event (e.g., one or more additional occurrenceof the event, etc.) using the second sensor data. The additional eventcan occur in the same wellbore or a different wellbore. For example, theone or more second event models can be trained and used in otherwellbores in the field to identify the presence and identification ofthe events in those other wellbores.

In some aspects, the event identification and corresponding dataobtained using the additional sensors can be used to calibrate existingmodels. In this context, training the one or more second event modelscan include a calibration process. In some aspects, the models orstructure of the model (e.g., the type of model, identification of themodel variables, etc.) can be known or pre-trained, and the eventidentification and corresponding data can be used as a new training dataset or used to supplement the original training data set to re-train theone or more second event models. For example, a model can be developedusing laboratory and/or testing data, and the event identification(e.g., using a known or induced event, using one or more first eventmodels, etc.) can be used with the second set of measurements tore-train or calibrate the developed model alone or in combination withthe laboratory or testing data. This process may allow the structure ofthe model (e.g., the features relied upon, the relationship of thefeatures, etc.) to remain the same while updating various derivedparameters of the model. For example, one or more parameters (e.g.,coefficients, weightings, etc.) can be updated or calibrated to providea more accurate model using data obtained from an actual in-situgeneration of the sensor data. This process may be useful to calibrateexisting models for specific wellbores, formations, or fields to improvethe event identifications in those locations and account for variationsbetween locations, wellbores, etc.

In some aspects, the calibration of the models can be used to identifycalibrations for one or more additional event models. As noted above,the identified labeled data can be used to re-train and/or calibrateexisting model(s), thereby updating one or more parameters of theexisting model(s). When the parameters of the existing model(s) areredetermined or calibrated, a calibration factor can be developed thatcan be applied to other existing model(s). The calibration factor canthen allow for one or more additional existing models to be updated toimprove the accuracy of the models without needing data derived from anin-situ occurrence of the event.

For example, an in-situ event such as gas injection within the wellborecan be determined based on having a known or induced event and/or usingdata within the wellbore along with one or more event models. Once theevent is identified, a second set of measurements can be obtained asdescribed herein, and along with the event identification, the secondset of measurements can be used to provide a labeled data set. In thisexample, acoustic data associated with gas injection can be obtainedduring the gas injection event. The resulting labeled data set can beused to calibrate one or more second event models used for detecting gasinjection using one or more frequency domain features derived from theacoustic data. An existing model may be developed based on test datasuch as simulating gas injection into a fluid in a test apparatus. Thestructure of the existing model (e.g., the specific one or morefrequency domain features used, and the relationship of the one or morefrequency domain features to each other) can be used in the trainingprocess with the labeled data set. When the parameters of the existingmodel are re-determined, a calibration factor that correlates to theoriginal parameters of the existing model, and updated parameters of thecalibrated model can be determined. The calibration factor can then beapplied to similar existing models such as a water injection model, ahydrocarbon injection model, a fluid flow model, and the like. Thecalibration factor can then help to adjust one or more existing modelsto more accurately reflect the parameters relevant to the location inwhich the in-situ data is obtained without the need for the specificevent identified by the model to occur.

The in-situ identification of training data can also be used tocross-check and validate existing models. For example, the in-situidentified data can be used to train the one or more second eventmodels. When an additional event is identified using the one or moresecond models, the event identification can be used to identifyadditional data using the first signals, which would correspond to thefirst set of measurements. The first set of models can be trained toverify whether or not the newly trained model matches the original modelwithin a given threshold. When the models match, the system can providean indication that the event is the only event present. When the modelsdo not match, it can be an indication that another, unidentified eventis present within the data. Additional training and event identificationcan then be used to identify the additional event. The cross-checkingand validation process can be carried out using subsequent data in time,at different depths along the wellbore, and/or across differentwellbores.

Application of the signal processing techniques and one or more eventdetection models for wellbore events such as downhole surveillance canprovide a number of benefits including improving reservoir recovery bymonitoring efficient drainage of reserves through downhole fluidsurveillance (e.g., production flow monitoring), improving welloperating envelopes through identification of drawdown levels (e.g.,gas, water, etc.), facilitating targeted remedial action for efficientwell management and well integrity, reducing operational risk throughthe clear identification of anomalies and/or failures in well barrierelements.

In some embodiments, use of the systems and methods described herein mayprovide knowledge of the events, including an identification of theevent(s), and the locations experiencing various events, therebypotentially allowing for improved actions (e.g., remediation actions forwellbore events, security actions for security events, etc.) based onthe processing results. The methods and systems disclosed herein canalso provide information on the events. For example, for wellboreevents, information about a variability of the amount of fluid inflowbeing produced by the different fluid influx zones as a function ofdifferent production rates, different production chokes, downholepressure conditions can be determined, different fluid outflow rates ininjection wells, fluid leak rates, equipment failures, and the like,thereby enabling control of fluid flow in the wellbore. Embodiments ofthe systems and methods disclosed herein can also allow for acomputation of the relative concentrations of fluid flow (e.g., relativeamounts of gas, hydrocarbon liquid, and water in the fluid flow) in thewellbore, thereby offering the potential for more targeted and effectiveremediation.

As disclosed herein, embodiments of the data processing techniques canuse various sequences of real time digital signal processing steps toidentify the temperature and/or acoustic signals resulting from variousevents from background noise, and allow real time detection of theevents and their locations using distributed fiber optic temperatureand/or acoustic sensor data as the input data feed.

One or more models can be developed using test data along withparameters for the event(s) to provide a labeled data set used as inputfor training the model. Since the data can be identified along with thecorresponding event during operation (e.g., during operation of awellbore), the data can be referred to as in-situ training data. Theresulting trained models can then be used to identify one or moresignatures based on features of the test data and one or more machinelearning techniques to develop correlations for the presence of variousevents. In the model development, specific events can be created in atest set-up, and the features of the model (e.g. temperature, signals,acoustic signals, pressure signals, flow signals, etc.) can be obtainedand recorded to develop test data. The test data can be used to trainone or more models defining the various events. The resulting model canthen be used to determine one or more events. In some embodiments,actual field data can be used and correlated to actual events usinginputs from, for example, other temperature sensors, other acousticsensors, and/or other production sensors (e.g., pressure sensors, flowmeters, optical sensors, etc.) to provide in-situ data used for trainingthe one or more models. The data can be labeled to create a trainingdata set based on actual production situations (e.g., in-situ data). Thedata can then be used alone or in combination with the test data todevelop the model(s). According to this disclosure, one or more eventmodels are trained using a second set of measurements of a second signalin a wellbore and identification of one or more events provided with afirst set of measurements of a first signal in the wellbore.

As described herein, the systems and methods can be used to identify thepresence and/or extent of one or more wellbore events. As noted above,various wellbore events can be determined using the system and method,such as, without limitation, fluid flow detection, fluid phasesegregation, fluid flow discrimination within a conduit, well integritymonitoring, in well leak detection, annular fluid flow diagnosis,overburden monitoring, fluid flow detection behind a casing, waxdeposition events, sand detection (e.g., sand ingress, sand flows,etc.), or the like. Fluid flow can comprise fluid flow along or within atubular within a wellbore such as fluid flow within a productiontubular. Fluid flow can also comprise fluid flow from the reservoir orformation into a wellbore tubular, and/or fluid flow from the wellboretubular into the reservoir (e.g., fluid injection). Such flow into thewellbore and/or a wellbore tubular can be referred to as fluid inflow.While fluid inflow may be separately identified at times in thisdisclosure in examples, such fluid inflow is considered a part of fluidflow within the wellbore.

In some aspects, the first set of measurement and/or the second set ofmeasurements can comprise temperature and/or acoustic measurements. Inthese aspects, temperature features and/or acoustic features can bedetermined from respective measurements taken along a length, forexample, a length of a wellbore. In some embodiments, the temperatureand/or acoustic measurements can be used with one or more temperatureand/or acoustic signatures, respectively, to determine the presence ofabsence of an event. The signatures can comprise a number of thresholdsor ranges for comparison with various temperature features. When thedetected temperature features fall within the signatures, the event maybe determined to be present. In some embodiments, temperaturemeasurements can be used in one or more first or event detection modelsthat can provide an output indicative of the presence or absence of oneor more events along the length of the wellbore. This can allow eventlocations to be identified using temperature based measurements from thewellbore. When combined with a distributed temperature sensing systemthat can provide distributed and continuous temperature measurements,the systems can allow for fluid inflow locations to be tracked throughtime. The identified event locations can be utilized as described hereinto identify data from a different physical parameter that can be used totrain one or more second event models.

A system of this disclosure will now be described with reference to aFIG. 3 , which is a schematic, cross-sectional illustration of adownhole wellbore operating environment 101 according to someembodiments. More specifically, environment 101 includes a wellbore 114traversing a subterranean formation 102, casing 112 lining at least aportion of wellbore 114, and a tubular 120 extending through wellbore114 and casing 112. A plurality of completion assemblies such as spacedscreen elements or assemblies 118 may be provided along tubular 120 atone or more production zones 104 a, 104 b within the subterraneanformation 102. In particular, two production zones 104 a, 104 b aredepicted within subterranean formation 102 of FIG. 3 ; however, theprecise number and spacing of the production zones 104 a, 104 b may bevaried in different embodiments. The completion assemblies can compriseflow control devices such as sliding sleeves, adjustable chokes, and/orinflow control devices to allow for control of the flow from eachproduction zone. The production zones 104 a, 104 b may be layers, zones,or strata of formation 102 that contain hydrocarbon fluids (e.g., oil,gas, condensate, etc.) therein.

In addition, a plurality of spaced zonal isolation devices 117 andgravel packs 122 may be provided between tubular 120 and the sidewall ofwellbore 114 at or along the interface of the wellbore 114 with theproduction zones 104 a, 104 b. In some embodiments, the operatingenvironment 101 includes a workover and/or drilling rig positioned atthe surface and extending over the wellbore 114. While FIG. 3 shows anexample completion configuration in FIG. 3 , it should be appreciatedthat other configurations and equipment may be present in place of or inaddition to the illustrated configurations and equipment. For example,sections of the wellbore 114 can be completed as open hole completionsor with gravel packs without completion assemblies.

In general, the wellbore 114 can be formed in the subterranean formation102 using any suitable technique (e.g., drilling). The wellbore 114 canextend substantially vertically from the earth's surface over a verticalwellbore portion, deviate from vertical relative to the earth's surfaceover a deviated wellbore portion, and/or transition to a horizontalwellbore portion. In general, all or portions of a wellbore may bevertical, deviated at any suitable angle, horizontal, and/or curved. Inaddition, the wellbore 114 can be a new wellbore, an existing wellbore,a straight wellbore, an extended reach wellbore, a sidetracked wellbore,a multi-lateral wellbore, and other types of wellbores for drilling andcompleting one or more production zones. As illustrated, the wellbore114 includes a substantially vertical producing section 150 whichincludes the production zones 104 a, 104 b. In this embodiment,producing section 150 is an open-hole completion (i.e., casing 112 doesnot extend through producing section 150). Although section 150 isillustrated as a vertical and open-hole portion of wellbore 114 in FIG.3 , embodiments disclosed herein can be employed in sections ofwellbores having any orientation, and in open or cased sections ofwellbores. The casing 112 extends into the wellbore 114 from the surfaceand can be secured within the wellbore 114 with cement 111.

The tubular 120 may comprise any suitable downhole tubular or tubularstring (e.g., drill string, casing, liner, jointed tubing, and/or coiledtubing, etc.), and may be inserted within wellbore 114 for any suitableoperation(s) (e.g., drilling, completion, intervention, workover,treatment, production, etc.). In the embodiment shown in FIG. 3 , thetubular 120 is a completion assembly string. In addition, the tubular120 may be disposed within in any or all portions of the wellbore 114(e.g., vertical, deviated, horizontal, and/or curved section of wellbore114).

In this embodiment, the tubular 120 extends from the surface to theproduction zones 104 a, 104 b and generally provides a conduit forfluids to travel from the formation 102 (particularly from productionzones 104 a, 104 b) to the surface. A completion assembly including thetubular 120 can include a variety of other equipment or downhole toolsto facilitate the production of the formation fluids from the productionzones. For example, zonal isolation devices 117 can be used to isolatethe production zones 104 a, 104 b within the wellbore 114. In thisembodiment, each zonal isolation device 117 comprises a packer (e.g.,production packer, gravel pack packer, frac-pac packer, etc.). The zonalisolation devices 117 can be positioned between the screen assemblies118, for example, to isolate different gravel pack zones or intervalsalong the wellbore 114 from each other. In general, the space betweeneach pair of adjacent zonal isolation devices 117 defines a productioninterval, and each production interval may correspond with one of theproduction zones 104 a, 104 b of subterranean formation 102.

The screen assemblies 118 provide sand control capability. Inparticular, the sand control screen elements 118, or other filter mediaassociated with wellbore tubular 120, can be designed to allow fluids toflow therethrough but restrict and/or prevent particulate matter ofsufficient size from flowing therethrough. The screen assemblies 118 canbe of any suitable type such as the type known as “wire-wrapped”, whichare made up of a wire closely wrapped helically about a wellboretubular, with a spacing between the wire wraps being chosen to allowfluid flow through the filter media while keeping particulates that aregreater than a selected size from passing between the wire wraps. Othertypes of filter media can also be provided along the tubular 120 and caninclude any type of structures commonly used in gravel pack wellcompletions, which permit the flow of fluids through the filter orscreen while restricting and/or blocking the flow of particulates (e.g.other commercially-available screens, slotted or perforated liners orpipes; sintered-metal screens; sintered-sized, mesh screens; screenedpipes; prepacked screens and/or liners; or combinations thereof). Aprotective outer shroud having a plurality of perforations therethroughmay be positioned around the exterior of any such filter medium.

The gravel packs 122 can be formed in the annulus 119 between the screenelements 118 (or tubular 120) and the sidewall of the wellbore 114 in anopen hole completion. In general, the gravel packs 122 compriserelatively coarse granular material placed in the annulus to form arough screen against the ingress of sand into the wellbore while alsosupporting the wellbore wall. The gravel pack 122 is optional and maynot be present in all completions.

In some embodiments, one or more of the completion assemblies cancomprise flow control elements such as sliding sleeves, chokes, valves,or other types of flow control devices that can control the flow of afluid from an individual production zone or a group of production zones.The force on the production face can then vary based on the type ofcompletion within the wellbore and/or each production zone (e.g., in asliding sleeve completion, open hole completion, gravel pack completion,etc.). In some embodiments, a sliding sleeve or other flow controlledproduction zone can experience a force on the production face that isrelatively uniform within the production zone, and the force on theproduction face can be different between each production zone. Forexample, a first production zone can have a specific flow controlsetting that allows the production rate from the first zone to bedifferent than the production rate from a second production zone. Thus,the choice of completion type (e.g., which can be specified in acompletion plan) can effect on the need for or the ability to provide adifferent production rate within different production zones.

Referring still to FIG. 3 , a monitoring system 110 can comprise anacoustic monitoring system and/or a temperature monitoring system. Themonitoring system 1110 can be positioned in the wellbore 114. Asdescribed herein, the monitoring system 110 may be utilized to detect ormonitor fluid inflow event(s) into the wellbore 114. The variousmonitoring systems (e.g., acoustic monitoring systems, temperaturemonitoring systems, etc.) may be referred to herein as an “flowdetection system,” and/or an “flow monitoring system.”

The monitoring system 110 comprises an optical fiber 162 that is coupledto and extends along tubular 120. In cased completions, the opticalfiber 162 can be installed between the casing and the wellbore wallwithin a cement layer and/or installed within the casing or productiontubing. Referring briefly to FIGS. 4A and 4B, optical fiber 162 of themonitoring system 110 may be coupled to an exterior of tubular 120(e.g., such as shown in FIG. 4B) or an interior of tubular (e.g., suchas shown in FIG. 4A). When the optical fiber 162 is coupled to theexterior of the tubular 120, as depicted in the embodiment of FIG. 4B,the optical fiber 162 can be positioned within a control line, controlchannel, or recess in the tubular 120. In some embodiments an outershroud contains the tubular 120 and protects the optical fiber 162during installation. A control line or channel can be formed in theshroud and the optical fiber 162 can be placed in the control line orchannel (not specifically shown in FIGS. 2A and 2B).

Referring again to FIG. 3 , generally speaking, during operation of athe monitoring system, an optical backscatter component of lightinjected into the optical fiber 162 may be used to detect variousconditions incident on the optical fiber such as acoustic perturbations(e.g., dynamic strain), temperature, static strain, and the like alongthe length of the optical fiber 162. The light can be generated by alight generator or source 166 such as a laser, which can generate lightpulses. The light used in the system is not limited to the visiblespectrum, and light of any frequency can be used with the systemsdescribed herein. Accordingly, the optical fiber 162 acts as the sensorelement with no additional transducers in the optical path, andmeasurements can be taken along the length of the entire optical fiber162. The measurements can then be detected by an optical receiver suchas sensor 164 and selectively filtered to obtain measurements from agiven depth point or range, thereby providing for a distributedmeasurement that has selective data for a plurality of zones (e.g.,production zones 104 a, 104 b) along the optical fiber 162 at any giventime. For example, time of flight measurements of the backscatteredlight can be used to identify individual zones or measurement lengths ofthe fiber optic 162. In this manner, the optical fiber 162 effectivelyfunctions as a distributed array of sensors spread over the entirelength of the optical fiber 162, which typically across production zones104 a, 104 b within the wellbore 114.

The light backscattered up the optical fiber 162 as a result of theoptical backscatter can travel back to the source, where the signal canbe collected by a sensor 164 and processed (e.g., using a processor168). In general, the time the light takes to return to the collectionpoint is proportional to the distance traveled along the optical fiber162, thereby allowing time of flight measurements of distance along theoptical fiber. The resulting backscattered light arising along thelength of the optical fiber 162 can be used to characterize theenvironment around the optical fiber 162. The use of a controlled lightsource 166 (e.g., having a controlled spectral width and frequency) mayallow the backscatter to be collected and any parameters and/ordisturbances along the length of the optical fiber 162 to be analyzed.In general, the various parameters and/or disturbances along the lengthof the optical fiber 162 can result in a change in the properties of thebackscattered light.

An acquisition device 160 may be coupled to one end of the optical fiber162 that comprises the sensor 164, light generator 166, a processor 168,and a memory 170. As discussed herein, the light source 166 can generatethe light (e.g., one or more light pulses), and the sensor 164 cancollect and analyze the backscattered light returning up the opticalfiber 162. In some contexts, the acquisition device 160 (which comprisesthe light source 166 and the sensor 164 as noted above), can be referredto as an interrogator. The processor 168 may be in signal communicationwith the sensor 164 and may perform various analysis steps described inmore detail herein. While shown as being within the acquisition device160, the processor 168 can also be located outside of the acquisitiondevice 160 including being located remotely from the acquisition device160. The sensor 164 can be used to obtain data at various rates and mayobtain data at a sufficient rate to detect the acoustic signals ofinterest with sufficient bandwidth. While described as a sensor 164 in asingular sense, the sensor 164 can comprise one or more photodetectorsor other sensors that can allow one or more light beams and/orbackscattered light to be detected for further processing. In anembodiment, depth resolution ranges in a range of from about 1 meter toabout 10 meters, or less than or equal to about 10, 9, 8, 7, 6, 5, 4, 3,2, or 1 meter can be achieved. Depending on the resolution needed,larger averages or ranges can be used for computing purposes. When ahigh depth resolution is not needed, a system may have a widerresolution (e.g., which may be less expensive) can also be used in someembodiments. Data acquired by the monitoring system 110 (e.g., via fiber162, sensor 164, etc.) may be stored on memory 170.

The monitoring system 110 can be used for detecting a variety ofparameters and/or disturbances in the wellbore including being used todetect temperatures along the wellbore, acoustic signals along thewellbore, static strain and/or pressure along the wellbore, or anycombination thereof.

In some embodiments, the monitoring system 110 can be used to detecttemperatures within the wellbore. The temperature monitoring system caninclude a distributed temperature sensing (DTS) system. A DTS system canrely on light injected into the optical fiber 162 along with thereflected signals to determine a temperature and/or strain based onoptical time-domain reflectometry. In order to obtain DTS measurements,a pulsed laser from the light generator 166 can be coupled to theoptical fiber 162 that serves as the sensing element. The injected lightcan be backscattered as the pulse propagates through the optical fiber162 owing to density and composition as well as to molecular and bulkvibrations. A portion of the backscattered light can be guided back tothe acquisition device 160 and split of by a directional coupler to asensor 164. It is expected that the intensity of the backscattered lightdecays exponentially with time. As the speed of light within the opticalfiber 162 is known, the distance that the light has passed through theoptical fiber 162 can be derived using time of flight measurements.

In both distributed acoustic sensing (DAS) and DTS systems, thebackscattered light includes different spectral components which containpeaks that are known as Rayleigh and Brillouin peaks and Raman bands.The Rayleigh peaks are independent of temperature and can be used todetermine the DAS components of the backscattered light. The Ramanspectral bands are caused by thermally influenced molecular vibrations.The Raman spectral bands can then be used to obtain information aboutdistribution of temperature along the length of the optical fiber 162disposed in the wellbore.

The Raman backscattered light has two components, Stokes andAnti-Stokes, one being only weakly dependent on temperature and theother being greatly influenced by temperature. The relative intensitiesbetween the Stokes and Anti-Stokes components and are a function oftemperature at which the backscattering occurred. Therefore, temperaturecan be determined at any point along the length of the optical fiber 162by comparing at each point the Stokes and Anti-stokes components of thelight backscattered from the particular point. The Brillouin peaks maybe used to monitor strain along the length of the optical fiber 162.

The DTS system can then be used to provide a temperature measurementalong the length of the wellbore during the production of fluids,including fluid inflow events. The DTS system can represent a separatesystem from the DAS system or a single common system, which can compriseone or more acquisition devices in some embodiments. In someembodiments, a plurality of fibers 162 are present within the wellbore,and the DAS system can be coupled to a first optical fiber and the DTSsystem can be coupled to a second, different, optical fiber.Alternatively, a single optical fiber can be used with both systems, anda time division multiplexing or other process can be used to measureboth DAS and DTS on the same optical fiber.

In an embodiment, depth resolution for the DTS system can range fromabout 1 meter to about 10 meters, or less than or equal to about 10, 9,8, 7, 6, 5, 4, 3, 2, or 1 meter can be achieved. Depending on theresolution needed, larger averages or ranges can be used for computingpurposes. When a high depth resolution is not needed, a system may havea wider resolution (e.g., which may be less expensive) can also be usedin some embodiments. Data acquired by the DTS system 110 (e.g., viafiber 162, sensor 164, etc.) may be stored on memory 170.

While the temperature monitoring system described herein can use a DTSsystem to acquire the temperature measurements for a location or depthrange in the wellbore 114, in general, any suitable temperaturemonitoring system can be used. For example, various point sensors,thermocouples, resistive temperature sensors, or other sensors can beused to provide temperature measurements at a given location based onthe temperature measurement processing described herein. Further, anoptical fiber comprising a plurality of point sensors such as Bragggratings can also be used. As described herein, a benefit of the use ofthe DTS system is that temperature measurements can be obtained across aplurality of locations and/or across a continuous length of the wellbore114 rather than at discrete locations.

The monitoring system 110 can comprise an acoustic monitoring system tomonitor acoustic signals within the wellbore. The acoustic monitoringsystem can comprise a DAS based system, though other types of acousticmonitoring systems, including other distributed monitoring systems, canalso be used.

During operation of a DAS system an optical backscatter component oflight injected into the optical fiber 162 (e.g., Rayleigh backscatter)may be used to detect acoustic perturbations (e.g., dynamic strain)along the length of the fiber 162. The light backscattered up theoptical fiber 162 as a result of the optical backscatter can travel backto the source, where the signal can be collected by a sensor 164 andprocessed (e.g., using a processor 168) as described herein. In general,any acoustic or dynamic strain disturbances along the length of theoptical fiber 162 can result in a change in the properties of thebackscattered light, allowing for a distributed measurement of both theacoustic magnitude (e.g., amplitude), frequency and, in some cases, ofthe relative phase of the disturbance. Any suitable detection methodsincluding the use of highly coherent light beams, compensatinginterferometers, local oscillators, and the like can be used to produceone or more signals that can be processed to determine the acousticsignals or strain impacting the optical fiber along its length.

While the system 101 described herein can be used with a DAS system(e.g., DAS system 110) to acquire an acoustic signal for a location ordepth range in the wellbore 114, in general, any suitable acousticsignal acquisition system can be used in performing embodiments ofmethod 10 (see e.g., FIG. 1 ). For example, various microphones,geophones, hydrophones, or other sensors can be used to provide anacoustic signal at a given location based on the acoustic signalprocessing described herein. Further, an optical fiber comprising aplurality of point sensors such as Bragg gratings can also be used. Asdescribed herein, a benefit of the use of the DAS system 110 is that anacoustic signal can be obtained across a plurality of locations and/oracross a continuous length of the wellbore 114 rather than at discretelocations.

The monitoring system 110 can be used to generate temperaturemeasurements and/or acoustic measurements along the length of thewellbore. The resulting measurements can be processed to obtain varioustemperature and/or acoustic based features that can then be used toidentify one or more events, including any of those described herein.Each of the specific types of features obtained from the monitoringsystem is described in more detail below.

The temperature features and/or frequency domain features can beunderstood by considering an example of fluid inflow into the wellbore.In this example, fluid can be produced into the wellbore 114 and intothe completion assembly string. During operations, the fluid flowinginto the wellbore may comprise hydrocarbon fluids, such as, for instancehydrocarbon liquids (e.g., oil), gases (e.g., natural gas such asmethane, ethane, etc.), and/or water, any of which can also compriseparticulates such as sand. However, the fluid flowing into the tubularmay also comprise other components, such as, for instance steam, carbondioxide, and/or various multiphase mixed flows. The fluid flow canfurther be time varying such as including slugging, bubbling, or timealtering flow rates of different phases. The amounts or flow rates ofthese components can vary over time based on conditions within theformation 102 and the wellbore 114. Likewise, the composition of thefluid flowing into the tubular 120 sections throughout the length of theentire production string (e.g., including the amount of sand containedwithin the fluid flow) can vary significantly from section to section atany given time.

Continuing with the example, as the fluid enters the wellbore 114, thefluid can create acoustic signals and temperature changes that can bedetected by the monitoring system such as the DTS system and/or the DASsystems as described herein. With respect to the temperature variations,the temperature changes can result from various fluid effects within thewellbore such as cooling based on gas entering the wellbore, temperaturechanges resulting from liquids entering the wellbore, and various flowrelated temperature changes as a result of the fluids passing throughthe wellbore. For example, as fluids enter the wellbore, the fluids canexperience a sudden pressure drop, which can result in a change in thetemperature. The magnitude of the temperature change depends on thephase and composition of the inflowing fluid, the pressure drop, and thepressure and temperature conditions. The other major thermodynamicprocess that takes place as the fluid enters the well is thermal mixingwhich results from the heat exchange between the fluid body that flowsinto the wellbore and the fluid that is already flowing in the wellbore.As a result, inflow of fluids from the reservoir into the wellbore cancause a deviation in the flowing well temperature profile. Other eventswithin the wellbore can also generate similar temperature and/oracoustic signals that can be used to identify the one or more events.

By obtaining the temperature in the wellbore, a number of temperaturefeatures can be obtained from the temperature measurements. Thetemperature features can provide an indication of one or moretemperature trends at a given location in the wellbore during ameasurement period. The resulting features can form a distribution oftemperature results that can then be used with various models toidentify one or more events within the wellbore at the location.

The temperature measurements can represent output values from the DTSsystem, which can be used with or without various types ofpre-processing such as noise reduction, smoothing, and the like. Whenbackground temperature measurements are used, the background measurementcan represent a temperature measurement at a location within thewellbore taken in the absence of the flow of a fluid. For example, atemperature profile along the wellbore can be taken when the well isinitially formed and/or the wellbore can be shut in and allowed toequilibrate to some degree before measuring the temperatures at variouspoints in the wellbore. The resulting background temperaturemeasurements or temperature profile can then be used in determining thetemperature features in some embodiments.

In general, the temperature features represent statistical variations ofthe temperature measurements through time and/or depth. For example, thetemperature features can represent statistical measurements or functionsof the temperature within the wellbore that can be used with variousmodels to determine whether or not fluid flow events have occurred. Thetemperature features can be determined using various functions andtransformations, and in some embodiments can represent a distribution ofresults. In some embodiments, the temperature features can represent anormal or Gaussian distribution. In some embodiments, the temperaturemeasurements can represent measurement through time and depth, such asvariations taken first with respect to time and then with respect todepth or first with respect to depth and then with respect to time. Theresulting distributions can then be used with models such asmultivariate models to determine the presence of the fluid flow events.

In some embodiments, the temperature features can include variousfeatures including, but not limited to, a depth derivative oftemperature with respect to depth, a temperature excursion measurement,a baseline temperature excursion, a peak-to-peak value, a Fast Fouriertransform (FFT), a Laplace transform, a wavelet transform, a derivativeof temperature with respect to depth, a heat loss parameter, anautocorrelation, and combinations thereof.

In some embodiments, the temperature features can comprise a depthderivative of temperature with respect to depth. This feature can bedetermined by taking the temperature measurements along the wellbore andsmoothing the measurements. Smoothing can comprise a variety of stepsincluding filtering the results, de-noising the results, or the like. Insome embodiments, the temperature measurements can be median filteredwithin a given window to smooth the measurements. Once smoothed, thechange in the temperature with depth can be determined. In someembodiments, this can include taking a derivative of the temperaturemeasurements with respect to depth along the longitudinal axis of thewellbore 114. The depth derivative of temperature values can then beprocessed, and the measurement with a zero value (e.g., representing apoint of no change in temperature with depth) that have preceding andproceeding values that are non-zero and have opposite signs in depth(e.g., zero below which the value is negative and above positive or viceversa) can have the values assign to the nearest value. This can thenresult in a set of measurements representing the depth derivative oftemperature with respect to depth.

In some embodiments, the temperature features can comprise a temperatureexcursion measurement. The temperature excursion measurement cancomprise a difference between a temperature reading at a first depth anda smoothed temperature reading over a depth range, where the first depthis within the depth range. In some embodiments, the temperatureexcursion measurement can represent a difference between de-trendedtemperature measurements over an interval and the actual temperaturemeasurements within the interval. For example, a depth range can beselected within the wellbore 114. The temperature readings within a timewindow can be obtained within the depth range and de-trended orsmoothed. In some embodiments, the de-trending or smoothing can includeany of those processes described above, such as using median filteringof the data within a window within the depth range. For medianfiltering, the larger the window of values used, the greater thesmoothing effect can be on the measurements. For the temperatureexcursion measurement, a range of windows from about 10 to about 100values, or between about 20-60 values (e.g., measurements of temperaturewithin the depth range) can be used to median filter the temperaturemeasurements. A difference can then be taken between the temperaturemeasurement at a location and the de-trended (e.g., median filtered)temperature values. The temperature measurements at a location can bewithin the depth range and the values being used for the medianfiltering. This temperature feature then represents a temperatureexcursion at a location along the wellbore 114 from a smoothedtemperature measurement over a larger range of depths around thelocation in the wellbore 114.

In some embodiments, the temperature features can comprise a baselinetemperature excursion. The baseline temperature excursion represents adifference between a de-trended baseline temperature profile and thecurrent temperature at a given depth. In some embodiments, the baselinetemperature excursion can rely on a baseline temperature profile thatcan contain or define the baseline temperatures along the length of thewellbore 114. As described herein, the baseline temperatures representthe temperature as measured when the wellbore 114 is shut in. This canrepresent a temperature profile of the formation in the absence of fluidflow. While the wellbore 114 may affect the baseline temperaturereadings, the baseline temperature profile can approximate a formationtemperature profile. The baseline temperature profile can be determinedwhen the wellbore 114 is shut in and/or during formation of the wellbore114, and the resulting baseline temperature profile can be used overtime. If the condition of the wellbore 114 changes over time, thewellbore 114 can be shut in and a new baseline temperature profile canbe measured or determined. It is not expected that the baselinetemperature profile is re-determined at specific intervals, and ratherit would be determined at discrete times in the life of the wellbore114. In some embodiments, the baseline temperature profile can bere-determined and used to determine one or more temperature featuressuch as the baseline temperature excursion.

Once the baseline temperature profile is obtained, the baselinetemperature measurements at a location in the wellbore 114 can besubtracted from the temperature measurement detected by the temperaturemonitoring system 110 at that location to provide baseline subtractedvalues. The results can then be obtained and smoothed or de-trended. Forexample, the resulting baseline subtracted values can be median filteredwithin a window to smooth the data. In some embodiments, a windowbetween 10 and 500 temperature values, between 50 and 400 temperaturevalues, or between 100 and 300 temperature values can be used to medianfilter the resulting baseline subtracted values. The resulting smoothedbaseline subtracted values can then be processed to determine a changein the smoothed baseline subtracted values with depth. In someembodiments, this can include taking a derivative of the smoothedbaseline subtracted values with respect to depth along the longitudinalaxis of the wellbore. The resulting values can represent the baselinetemperature excursion feature.

In some embodiments, the temperature features can comprise apeak-to-peak temperature value. This feature can represent thedifference between the maximum and minimum values (e.g., the range,etc.) within the temperature profile along the wellbore 114. In someembodiments, the peak-to-peak temperature values can be determined bydetecting the maximum temperature readings (e.g., the peaks) and theminimum temperature values (e.g., the dips) within the temperatureprofile along the wellbore 114. The difference can then be determinedwithin the temperature profile to determine peak-to-peak values alongthe length of the wellbore 114. The resulting peak-to-peak values canthen be processed to determine a change in the peak-to-peak values withrespect to depth. In some embodiments, this can include taking aderivative of the peak-to-peak values with respect to depth along thelongitudinal axis of the wellbore 114. The resulting values canrepresent the peak-to-peak temperature values.

Other temperature features can also be determined from the temperaturemeasurements. In some embodiments, various statistical measurements canbe obtained from the temperature measurements along the wellbore 114 todetermine one or more temperature features. For example, across-correlation of the temperature measurements with respect to timecan be used to determine a cross-correlated temperature feature. Thetemperature measurements can be smoothed as described herein prior todetermining the cross-correlation with respect to time. As anotherexample, an autocorrelation measurement of the temperature measurementscan be obtained with respect to depth. Autocorrelation is defined as thecross-correlation of a signal with itself. An autocorrelationtemperature feature can thus measure the similarity of the signal withitself as a function of the displacement. An autocorrelation temperaturefeature can be used, in applications, as a means of anomaly detectionfor one or more events (e.g., fluid flow, fluid leaks, sand ingress,etc.). The temperature measurements can be smoothed and/or the resultingautocorrelation measurements can be smoothed as described herein todetermine the autocorrelation temperature features.

In some embodiments, the temperature features can comprise a FastFourier transform (FFT) of the distributed temperature sensing (e.g.,DTS) signal. This algorithm can transform the distributed temperaturesensing signal from the time domain into the frequency domain, thusallowing detection of the deviation in DTS along length (e.g., depth).This temperature feature can be utilized, for example, for anomalydetection for one or more events.

In some embodiments, the temperature features can comprise the Laplacetransform of DTS. This algorithm can transform the DTS signal from thetime domain into Laplace domain allows us to detect the deviation in theDTS along length (e.g., depth of wellbore 114). This temperature featurecan be utilized, for example, for anomaly detection for event detection.This feature can be utilized, for example, in addition to (e.g., incombination with) the FFT temperature feature.

In some embodiments, the temperature features can comprise a wavelettransform of the distributed temperature sensing (e.g., DTS) signaland/or of the derivative of DTS with respect to depth, dT/dz. Thewavelet transform can be used to represent the abrupt changes in thesignal data. This feature can be utilized, for example, in fluid flowdetection. A wavelet is described as an oscillation that has zero mean,which can thus make the derivative of DTS in depth more suitable forthis application. In embodiments and without limitation, the wavelet cancomprise a Morse wavelet, an Analytical wavelet, a Bump wavelet, or acombination thereof.

In some embodiments, the temperature features can comprise a derivativeof DTS with respect to depth, or dT/dz. The relationship between thederivative of flowing temperature T_(f) with respect to depth (L) (e.g.,dT_(f)/dL) has been described by several models. For example, andwithout limitation, the model described by Sagar (Sagar, R., Doty, D.R., & Schmidt, Z. (1991, November 1). Predicting Temperature Profiles ina Flowing Well. Society of Petroleum Engineers. doi:10.2118/19702-PA)which accounts for radial heat loss due to conduction and describes arelationship (Equation (1) below) between temperature change in depthand mass rate. The mass rate w_(t) is conversely proportional to therelaxation parameter A and, as the relaxation parameter A increases, thechange in temperature in depth increases. Hence this temperature featurecan be designed to be used, for example, in events comprising flowquantification.

$\begin{matrix}{\frac{{dT}_{f}}{dL} = {- {{A\left\lbrack {\left( {T_{f} - T_{e}} \right) + {\frac{g}{g_{c}}\frac{\sin\theta}{{JC}_{pm}A}} - \frac{F_{c}}{A}} \right\rbrack}.}}} & (1)\end{matrix}$The formula for the relaxation parameter, A, is provided in Equation(2):

$\begin{matrix}{A = {\left( \frac{2\pi}{w_{t}C_{pl}} \right)\left( \frac{r_{ti}Uk_{e}}{k_{e} + {r_{ti}{{Uf}/12}}} \right)\left( \frac{1}{86,400 \times 12} \right)}} & (2)\end{matrix}$

-   -   A=coefficient, ft⁻¹    -   C_(pL)=specific heat of liquid, Btu/lbm-° F.    -   C_(pm)=specific heat of mixture, Btu/lbm-° F.    -   C_(po)=specific heat of oil, Btu/lbm-° F.    -   C_(pw)=specific heat of water, Btu/lbm-° F.    -   d_(c)=casing diameter, in.    -   d_(t)=tubing diameter, in.    -   d_(wb)=wellbore diameter, in.    -   D=depth, ft    -   D_(inj)=injection depth, ft    -   f=modified dimensionless beat conduction time function for long        times for earth    -   f(t)=dimensionless transient heat conduction time function for        earth    -   F_(c)=correction factor    -   F_(c) =average correction factor for one length interval    -   g=acceleration of gravity, 32.2 ft/sec²    -   g_(c)=conversion factor, 32.2 ft-lbm/sec²-lbf    -   g_(G)=geothermal gradient, ° F./ft    -   h=specific enthalpy, Btu/lbm    -   J=mechanical equivalent of heat, 778 ft-lbf/Btu    -   k_(an)=thermal conductivity of material in annulus, Btu/D-ft-°        F.    -   k_(ang)=thermal conductivity of gas in annulus, Btu/D-ft-° F.    -   k_(anw)=thermal conductivity of water in annulus, Btu/D-ft-° F.    -   k_(cem)=thermal conductivity of cement, Btu/D-ft-° F.    -   k_(e)=thermal conductivity of earth, Btu/D-ft-° F.    -   L=length of well fem perforations, ft    -   L_(in)=length from perforation to inlet, ft    -   p=pressure, psi    -   p_(wh)=wellhead pressure, psig    -   q_(gf)=formation gas flow rate, scf/D    -   q_(ginj)=injection gas flow rate, scf/D    -   q_(o)=oil flow rate, STB/D    -   q_(w)=water Dow rate, STB/D    -   Q=hem transfer between fluid and surrounding area, Btu/lbm    -   r_(ci)=inside casing radius, in.    -   r_(co)=outside casing radius, in.    -   r_(ti)=inside tubing radius, in.    -   r_(to)=outside tubing radius, in.    -   r_(wb)=wellbore radius, in.    -   R_(gL)=gas/liquid ratio, scf/STB    -   T=temperature, ° F.    -   T_(bh)=bottomhole temperature, ° F.    -   T_(c)=casing temperature, ° F.    -   T_(e)=surrounding earth temperature, ° F.    -   T_(ein)=earth temperature at inlet, ° F.    -   T_(f)=flowing fluid temperature, ° F.    -   T_(fin)=flowing fluid temperature at inlet, ° F.    -   T_(h)=cement/earth interface temperature, ° F.    -   U=overall heat transfer coefficient, Btu/D-ft²-° F.    -   v=fluid velocity, ft/sec    -   V=volume    -   w_(t)=total mass flow rate, lbm/sec    -   Z=height from bottom of hole, ft    -   Z_(in)=height from bottom of hole at inlet, ft    -   α=thermal diffusivity of earth, 0.04 ft²/hr    -   γ_(API)=oil gravity, °API    -   γ_(g)=gas specific gravity (air=1)    -   γ_(o)=oil specific gravity    -   γ_(w)=water specific gravity    -   θ=angle of inclination, degrees    -   μ=Joule-Thomson coefficient

In some embodiments, the temperature features can comprise a heat lossparameter. As described hereinabove, Sagars model describes therelationship between various input parameters, including the mass ratew_(t) and temperature change in depth dT_(f)/dL. These parameters can beutilized as temperature features in a machine learning model which usesfeatures from known cases (production logging results) as learning datasets, when available. These features can include geothermal temperature,deviation, dimensions of the tubulars 120 that are in the well (casing112, tubing 120, gravel pack 122 components, etc.), as well as thewellbore 114, well head pressure, individual separator rates, downholepressure, gas/liquid ratio, and/or a combination thereof. Such heat lossparameters can, for example, be utilized as inputs in a machine learningmodel for events comprising fluid flow quantification of the mass flowrate w_(t).

In some embodiments, the temperature features an comprise a time-depthderivative and/or a depth-time derivative. A temperature featurecomprising a time-depth derivative can comprise a change in atemperature measurement at one or more locations across the wellboretaken first with respect to time, and a change in the resulting valueswith respect to depth can then be determined. Similarly, a temperaturefeature comprising a depth-time derivative can comprise a change in atemperature measurement at one or more locations across the wellboretaken first with respect to depth, and a change in the resulting valueswith respect to time can then be determined.

In some embodiments, the temperature features can be based on dynamictemperature measurements rather than steady state or flowing temperaturemeasurements. In order to obtain dynamic temperature measurements, achange in the operation of the system (e.g., wellbore) can beintroduced, and the temperature monitored using the temperaturemonitoring system. For example in a wellbore environment, the change inconditions can be introduced by shutting in the wellbore, opening one ormore sections of the wellbore to flow, introducing a fluid to thewellbore (e.g., injecting a fluid), and the like. When the wellbore isshut in from a flowing state, the temperature profile along the wellboremay be expected to change from the flowing profile to the baselineprofile over time. Similarly, when a wellbore that is shut in is openedfor flow, the temperature profile may change from a baseline profile toa flowing profile. Based on the change in the condition of the wellbore,the temperature measurements can change dynamically over time. In someembodiments, this approach can allow for a contrast in thermalconductivity to be determined between a location or interval havingradial flow (e.g., into or out of the wellbore) to a location orinterval without radial flow. One or more temperature features can thenbe determined using the dynamic temperature measurements. Once thetemperature features are determined from the temperature measurementsobtained from the temperature monitoring system, one or more of thetemperature features can be used to identify events along the lengthbeing monitored (e.g., within the wellbore), as described in more detailherein.

As described with respect to the temperature measurements, the flow offluids in the wellbore 114 an also create acoustic sounds that can bedetected using the acoustic monitoring system such as a DAS system.Accordingly, the flow of the various fluids in the wellbore 114 and/orthrough the wellbore 114 can create vibrations or acoustic sounds thatcan be detected using acoustic monitoring system. Each type of fluidflow event such as the different fluid flows and fluid flow locationscan produce an acoustic signature with unique frequency domain features.Other events such as leaks, overburden movements, equipment failures,and the like (e.g., any of the events described herein) can also createacoustic signals that can have a unique relationship between one or morefrequency domain features.

As used herein, various frequency domain features can be obtained fromthe acoustic signal, and in some contexts, the frequency domain featurescan also be referred to herein as spectral features or spectraldescriptors. The frequency domain features are features obtained from afrequency domain analysis of the acoustic signals obtained within thewellbore. The frequency domain features can be derived from the fullspectrum of the frequency domain of the acoustic signal such that eachof the frequency domain features can be representative of the frequencyspectrum of the acoustic signal. Further, a plurality of differentfrequency domain features can be obtained from the same acoustic signal(e.g., the same acoustic signal at a location or depth within thewellbore), where each of the different frequency domain features isrepresentative of frequencies across the same frequency spectrum of theacoustic signal as the other frequency domain features. For example, thefrequency domain features (e.g., each frequency domain feature) can be astatistical shape measurement or spectral shape function of the spectralpower measurement across the same frequency bandwidth of the acousticsignal. Further, as used herein, frequency domain features can alsorefer to features or feature sets derived from one or more frequencydomain features, including combinations of features, mathematicalmodifications to the one or more frequency domain features, rates ofchange of the one or more frequency domain features, and the like.

The frequency domain features can be determined by processing theacoustic signals from within the wellbore at one or more locations alongthe wellbore. As the acoustics signals at a given location along thewellbore contain a combination of acoustic signals, the determination ofthe frequency domain features can be used to separate and identifyindividual events. As an example, FIG. 3 illustrates sand 202 flowingfrom the formation 102 into the wellbore 114 and then into the tubular120. As the sand 202 flows into the tubular 120, it can collide againstthe inner surface 204 of the tubular 120, and with the fiber 162 (e.g.,in cases where the fiber 162 is placed within the tubular 120), in arandom fashion. Without being limited by this or any particular theory,the intensity of the collisions depends on the effective mass and therate of change in the velocity of the impinging sand particles 202,which can depend on a number of factors including, without limitation,the direction of travel of the sand 202 in the wellbore 114 and/ortubular 120. The resulting random impacts can produce a random,broadband acoustic signal that can be captured on the optical fiber 162coupled (e.g., strapped) to the tubular 120. The random excitationresponse tends to have a broadband acoustic signal with excitationfrequencies extending up to the high frequency bands, for example, up toand beyond about 5 kHz depending on the size of the sand particles 202.In general, larger particle sizes may produce higher frequencies. Theintensity of the acoustic signal may be proportional to theconcentration of sand 202 generating the excitations such that anincreased broad band power intensity can be expected at increasing sand202 concentrations. In some embodiments, the resulting broadbandacoustic signals that can be identified can include frequencies in therange of about 5 Hz to about 10 kHz, frequencies in the range of about 5Hz to about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in therange of about 500 Hz to about 5 kHz. Any frequency ranges between thelower frequencies values (e.g., 5 Hz, 50 Hz, 500 Hz, etc.) and the upperfrequency values (e.g., 10 kHz, 7 kHz, 5 kHz, etc.) can be used todefine the frequency range for a broadband acoustic signal.

In addition to the sand entering the wellbore, fluid flow at thelocation can also create acoustic signals along with fluid flowingaxially or longitudinally through the wellbore. Background noise canalso be present. Other acoustic signal sources can include fluid flowwith or without sand 202 through the formation 102, fluid flow with orwithout sand 202 through a gravel pack 122, fluid flow with or withoutsand 202 within or through the tubular 120 and/or sand screen 118, fluidflow with sand 202 within or through the tubular 120 and/or sand screen118, fluid flow without sand 202 into the tubular 120 and/or sand screen118, gas/liquid flow, hydraulic fracturing, fluid leaks pastrestrictions (e.g., gas leaks, liquid leaks, etc.) mechanicalinstrumentation and geophysical acoustic noises and potential pointreflection noise within the fiber caused by cracks in the fiber opticcable/conduit under investigation. The combined acoustic signal can thenbe detected by the acoustic monitoring system. In order to detect one ormore of these events, the acoustic signal can be processed to determineone or more frequency domain features of the acoustic signal at a depthin the wellbore.

In order to determine the frequency domain features, an acoustic signalcan be obtained using the acoustic monitoring system during operation ofthe wellbore. The resulting acoustic signal can be optionallypre-processed using a number of steps. Depending on the type of DASsystem employed, the optical data may or may not be phase coherent andmay be pre-processed to improve the signal quality (e.g., denoised foropto-electronic noise normalization/de-trending single point-reflectionnoise removal through the use of median filtering techniques or eventhrough the use of spatial moving average computations with averagingwindows set to the spatial resolution of the acquisition unit, etc.).The raw optical data from the acoustic sensor can be received,processed, and generated by the sensor to produce the acoustic signal.

In some embodiments, a processor or collection of processors (e.g.,processor 168 in FIG. 3 ) may be utilized to perform the optionalpre-processing steps described herein. In an embodiment, the noisedetrended “acoustic variant” data can be subjected to an optionalspatial filtering step following the other pre-processing steps, ifpresent. A spatial sample point filter can be applied that uses a filterto obtain a portion of the acoustic signal corresponding to a desireddepth or depth range in the wellbore. Since the time the light pulsesent into the optical fiber returns as backscattered light cancorrespond to the travel distance, and therefore depth in the wellbore,the acoustic data can be processed to obtain a sample indicative of thedesired depth or depth range. This may allow a specific location withinthe wellbore to be isolated for further analysis. The pre-processing mayalso include removal of spurious back reflection type noises at specificdepths through spatial median filtering or spatial averaging techniques.This is an optional step and helps focus primarily on an interval ofinterest in the wellbore. For example, the spatial filtering step can beused to focus on a producing interval where there is high likelihood ofsand ingress, for example. The resulting data set produced through theconversion of the raw optical data can be referred to as the acousticsample data.

The acoustic data, including the optionally pre-processed and/orfiltered data, can be transformed from the time domain into thefrequency domain using a transform. For example, a Fourier transformsuch as a Discrete Fourier transformations (DFT), a short time Fouriertransform (STFT), or the like can be used to transform the acoustic datameasured at each depth section along the fiber or a section thereof intoa frequency domain representation of the signal. The resulting frequencydomain representation of the data can then be used to provide the datafrom which the plurality of frequency domain features can be determined.Spectral feature extraction using the frequency domain features throughtime and space can be used to determine one or more frequency domainfeatures.

The use of frequency domain features to identify fluid flow events andlocations, flow phase identification, and/or flow quantities of one ormore fluid phases can provide a number of advantages. First, the use offrequency domain features results in significant data reduction relativeto the raw DAS data stream. Thus, a number of frequency domain featurescan be calculated and used to allow for event identification while theremaining data can be discarded or otherwise stored, and the remaininganalysis can performed using the frequency domain features. Even whenthe raw DAS data is stored, the remaining processing power issignificantly reduced through the use of the frequency domain featuresrather than the raw acoustic data itself. Further, the use of thefrequency domain features can, with the appropriate selection of one ormore of the frequency domain features, provide a concise, quantitativemeasure of the spectral character or acoustic signature of specificsounds pertinent to downhole fluid surveillance and other applications.

While a number of frequency domain features can be determined for theacoustic sample data, not every frequency domain feature may be used toidentify fluid flow events and locations, flow phase identification,and/or flow quantities of one or more fluid phases. The frequency domainfeatures represent specific properties or characteristics of theacoustic signals.

In some embodiments, combinations of frequency domain features can beused as the frequency domain features themselves, and the resultingcombinations are considered to be part of the frequency domain featuresas described herein. In some embodiments, a plurality of frequencydomain features can be transformed to create values that can be used todefine various event signatures. This can include mathematicaltransformations including ratios, equations, rates of change, transforms(e.g., wavelets, Fourier transforms, other wave form transforms, etc.),other features derived from the feature set, and/or the like as well asthe use of various equations that can define lines, surfaces, volumes,or multi-variable envelopes. The transformation can use othermeasurements or values outside of the frequency domain features as partof the transformation. For example, time domain features, other acousticfeatures, and non-acoustic measurements can also be used. In this typeof analysis, time can also be considered as a factor in addition to thefrequency domain features themselves. As an example, a plurality offrequency domain features can be used to define a surface (e.g., aplane, a three-dimensional surface, etc.) in a multivariable space, andthe measured frequency domain features can then be used to determine ifthe specific readings from an acoustic sample fall above or below thesurface. The positioning of the readings relative to the surface canthen be used to determine if the event is present or not at thatlocation in that detected acoustic sample.

The frequency domain features can include any frequency domain featuresderived from the frequency domain representations of the acoustic data.Such frequency domain features can include, but are not limited to, thespectral centroid, the spectral spread, the spectral roll-off, thespectral skewness, the root mean square (RMS) band energy (or thenormalized sub-band energies/band energy ratios), a loudness or totalRMS energy, a spectral flatness, a spectral slope, a spectral kurtosis,a spectral flux, a spectral autocorrelation function, or a normalizedvariant thereof.

The spectral centroid denotes the “brightness” of the sound captured bythe optical fiber (e.g., optical fiber 162 shown in FIG. 3 ) andindicates the center of gravity of the frequency spectrum in theacoustic sample. The spectral centroid can be calculated as the weightedmean of the frequencies present in the signal, where the magnitudes ofthe frequencies present can be used as their weights in someembodiments.

The spectral spread is a measure of the shape of the spectrum and helpsmeasure how the spectrum is distributed around the spectral centroid. Inorder to compute the spectral spread, Si, one has to take the deviationof the spectrum from the computed centroid as per the following equation(all other terms defined above):

$\begin{matrix}{S_{i} = {\sqrt{\frac{\sum\limits_{k = 1}^{N}{\left( {{f(k)} - C}_{i} \right)^{2}{X_{i}(k)}}}{\sum\limits_{k = 1}^{N}{X_{i}(k)}}}.}} & \left( {{Eq}.2} \right)\end{matrix}$

The spectral roll-off is a measure of the bandwidth of the audio signal.The Spectral roll-off of the i^(th) frame, is defined as the frequencybin ‘y’ below which the accumulated magnitudes of the short-time Fouriertransform reach a certain percentage value (usually between 85%-95%) ofthe overall sum of magnitudes of the spectrum.

$\begin{matrix}{{{\sum\limits_{k = 1}^{y}{❘{X_{i}(k)}❘}} = {\frac{c}{100}{\sum\limits_{k = 1}^{N}{❘{X_{i}(k)}❘}}}},} & \left( {{Eq}.3} \right)\end{matrix}$

where c=85 or 95. The result of the spectral roll-off calculation is abin index and enables distinguishing acoustic events based on dominantenergy contributions in the frequency domain (e.g., between gas influxand liquid flow, etc.).

The spectral skewness measures the symmetry of the distribution of thespectral magnitude values around their arithmetic mean.

The RMS band energy provides a measure of the signal energy withindefined frequency bins that may then be used for signal amplitudepopulation. The selection of the bandwidths can be based on thecharacteristics of the captured acoustic signal. In some embodiments, asub-band energy ratio representing the ratio of the upper frequency inthe selected band to the lower frequency in the selected band can rangebetween about 1.5:1 to about 3:1. In some embodiments, the sub-bandenergy ratio can range from about 2.5:1 to about 1.8:1, or alternativelybe about 2:1 The total RMS energy of the acoustic waveform calculated inthe time domain can indicate the loudness of the acoustic signal. Insome embodiments, the total RMS energy can also be extracted from thetemporal domain after filtering the signal for noise.

The spectral flatness is a measure of the noisiness/tonality of anacoustic spectrum. It can be computed by the ratio of the geometric meanto the arithmetic mean of the energy spectrum value and may be used asan alternative approach to detect broad-banded signals. For tonalsignals, the spectral flatness can be close to 0 and for broader bandsignals it can be closer to 1.

The spectral slope provides a basic approximation of the spectrum shapeby a linearly regressed line. The spectral slope represents the decreaseof the spectral amplitudes from low to high frequencies (e.g., aspectral tilt). The slope, the y-intersection, and the max and mediaregression error may be used as features.

The spectral kurtosis provides a measure of the flatness of adistribution around the mean value.

The spectral flux is a measure of instantaneous changes in the magnitudeof a spectrum. It provides a measure of the frame-to-frame squareddifference of the spectral magnitude vector summed across allfrequencies or a selected portion of the spectrum. Signals with slowlyvarying (or nearly constant) spectral properties (e.g., noise) have alow spectral flux, while signals with abrupt spectral changes have ahigh spectral flux. The spectral flux can allow for a direct measure ofthe local spectral rate of change and consequently serves as an eventdetection scheme that could be used to pick up the onset of acousticevents that may then be further analyzed using the feature set above toidentify and uniquely classify the acoustic signal.

The spectral autocorrelation function provides a method in which thesignal is shifted, and for each signal shift (lag) the correlation orthe resemblance of the shifted signal with the original one is computed.This enables computation of the fundamental period by choosing the lag,for which the signal best resembles itself, for example, where theautocorrelation is maximized. This can be useful in exploratorysignature analysis/even for anomaly detection for well integritymonitoring across specific depths where well barrier elements to bemonitored are positioned.

Any of these frequency domain features, or any combination of thesefrequency domain features (including transformations of any of thefrequency domain features and combinations thereof), can be used todetect and identify one or more events and locations. In some aspects, aselected set of characteristics can be used to identify the events,and/or all of the frequency domain features that are calculated can beused as a group in characterizing the identity and location of the oneor more events. The specific values for the frequency domain featuresthat are calculated can vary depending on the specific attributes of theacoustic signal acquisition system, such that the absolute value of eachfrequency domain feature can change between systems. In some aspects,the frequency domain features can be calculated for each event based onthe system being used to capture the acoustic signal and/or thedifferences between systems can be taken into account in determining thefrequency domain feature values for each fluid inflow event between oramong the systems used to determine the values and the systems used tocapture the acoustic signal being evaluated. For example, the frequencydomain features can be normalized based on the acquired values toprovide more consistent readings between systems and locations.

One or a plurality of frequency domain features can be used to identifyevents and locations. In an embodiment, one, or at least two, three,four, five, six, seven, eight, etc. different frequency domain featurescan be used to identify the one or more events and their locations. Thefrequency domain features can be combined or transformed in order todefine the event signatures for one or more events, such as, forinstance, a fluid flow event location or flowrate. While exemplarynumerical ranges are provided herein, the actual numerical results mayvary depending on the data acquisition system and/or the values can benormalized or otherwise processed to provide different results.

In embodiments, the method 10 of identifying one or more events withinwellbore 114 further comprises creating labeled data using theidentified one or more events identified at 13 and the second set ofmeasurements obtained at 15.

As depicted in FIG. 2A, which is a flow diagram of identifying one ormore events within the wellbore 114 using the first set of measurementsat 13, in embodiments, identifying the one or more events at 13comprises: using the first set of measurements with one or more wellboreevent models at 13′; and identifying the one or more events with the oneor more wellbore event models at 13″. For example, when the first set ofmeasurements comprises DTS measurements, the first set of (e.g., DTS)measurements can be utilized as described hereinabove with one or morewellbore event models to identify the one or more events. For example,as depicted in FIG. 2B, in such embodiments, identifying one or moreevents within the wellbore 114 using the first set of measurements at 13can comprise determining temperature features at 13′″, using thetemperature features with one or more wellbore event models at 13′, anddetermining the presence of (i.e., “identifying”) the one or more events(such as, without limitation, fluid flow) at one or more locations inthe wellbore 114 using an output from the one or more wellbore eventmodels at 13″.

In some aspects, the one or more wellbore event models can comprisephysics, fluid mechanics, or first principles models. For example,temperature based measurements can be used in a first principles modelto identify the inflow of a gas phase hydrocarbon into the wellbore.Various phenomena such as Joule-Thomson cooling can result in alocalized temperature change to identify the inflow of gas. Other firstprinciples or similar type models can also be used to identify the oneor more events at 13. In some aspects, the one or more wellbore eventmodels can comprise a plurality of models using different parameters.For example, first principles models can be combined with temperaturebased machine learning models to fully identify the one or more eventsat 13.

In embodiments, subsequent training of the one or more event models at17, the method 10 can further comprise at 19 (e.g., using the one ormore event models to identify the at least one additional event withinthe wellbore 114): monitoring the first signal within the wellbore 114;monitoring the second signal within the wellbore 114; using the firstsignal in the one or more wellbore event models; using the second signalin the (now trained) one or more event models; and detecting the atleast one additional event based on outputs of both the one or morewellbore event models and the one or more event models. In this manner,the trained one or more event models and the one or more wellbore eventmodels utilized to identify the one or more events at 13 that weresubsequently utilized to train the one or more event models at 17 can beutilized at 19 to identify the at least one additional event within thewellbore 114.

In some aspects, the second signal can comprise an acoustic signal. Insuch embodiments, a method of identifying events within a wellboreaccording to this disclosure can comprise: obtaining a first set ofmeasurements of a first signal within a wellbore 114 at 11; identifyingone or more events within the wellbore 114 at 13; obtaining an acousticdata set from within the wellbore 114 at 15, wherein the first signal isnot an acoustic signal; training, at 17, one or more second event modelsusing the acoustic data set and the identification of the one or moreevents as inputs; and using the trained one or more second event modelsat 19 to identify at least one additional event within the wellbore 114.

As noted hereinabove, the second signal comprises an acoustic signal,the first set of measurements can comprise distributed temperaturesensor (DTS) measurements. Alternatively or additionally, the first setof measurements can comprise production volumetric (e.g., PLT)information. Identifying the one or more events within the wellbore 114at 13 can comprise: identifying a first location having a first event ofthe one or more events; and identifying the first event at the firstlocation using one or more wellbore event models.

Referring to FIG. 1 , training the one or more event models at 17 cancomprise: obtaining acoustic data for the first location from theacoustic data set (e.g., as described hereinabove with reference to FIG.3 ); and training the one or more event models using the acoustic datafor the first location and the identification of the first event at thefirst location. Using the trained one or more event models at 19 toidentify the at least one additional event within the wellbore 114 cancomprise using the one or more trained event models (optionally inconjunction with the one or more wellbore event models) to identify theat least one additional event. The event can be identified along thelength of the wellbore 114, or in some aspects, within another wellbore.

For an event comprising fluid flow, one or more fluids that can includegas, a liquid aqueous phase, a liquid hydrocarbon phase, and potentiallyother fluids as well as various combinations thereof can enter thewellbore 114 at one or more locations along the wellbore 114.Temperature features can be utilized to identify these inflow locations.As noted hereinabove, temperature features can be utilized with one ormore first or wellbore event models to provide an output of the one ormore first or wellbore event models and then be utilized with the one ormore second event models to provide an output of the second model.Subsequent to training of the one or more event models, the presence(and/or extent) of the at least one additional event at one or morelocations can be determined using an output from the one or more firstor wellbore event models, an output from the one or more second eventmodels, or a combined output obtained using the output from the one ormore first or wellbore event models and the output from the one or moresecond event models.

The temperature features can be determined using the temperaturemonitoring system to obtain temperature measurements along the lengthbeing monitored (e.g., the length of the wellbore). In some embodiments,a DTS system can be used to receive distributed temperature measurementsignals from a sensor disposed along the length (e.g., the length of thewellbore), such as an optical fiber. The resulting signals from thetemperature monitoring system can be used to determine one or moretemperature features as described herein. In some embodiments, abaseline or background temperature profile can be used to determine thetemperature features, and the baseline temperature profile can beobtained prior to obtaining the temperature measurements.

In some embodiments, a plurality of temperature features can bedetermined from the temperature measurements, and the plurality oftemperature features can comprise at least two of: a depth derivative oftemperature with respect to depth, a temperature excursion measurement,a baseline temperature excursion, a peak-to-peak value, a fast Fouriertransform, a Laplace transform, a wavelet transform, a derivative oftemperature with respect to length (e.g., depth), a heat loss parameter,an autocorrelation, as detailed hereinabove, and/or the like. Othertemperature features can also be used in some embodiments. Thetemperature excursion measurement can comprise a difference between atemperature reading at a first depth, and a smoothed temperature readingover a depth range, where the first depth is within the depth range. Thebaseline temperature excursion can comprise a derivative of a baselineexcursion with depth, where the baseline excursion can comprise adifference between a baseline temperature profile and a smoothedtemperature profile. The peak-to-peak value can comprise a derivative ofa peak-to-peak difference with depth, where the peak-to-peak differencecomprises a difference between a peak high temperature reading and apeak low temperature reading with an interval. The fast FourierTransform can comprise an FFT of the distributed temperature sensingsignal. The Laplace transform can comprise a Laplace transform of thedistributed temperature sensing signal. The wavelet transform cancomprise a wavelet transform of the distributed temperature sensingsignal or of the derivative of the distributed temperature sensingsignal with respect to length (e.g., depth). The derivative of thedistributed temperature sensing signal with respect to length (e.g.,depth) can comprise the derivative of the flowing temperature withrespect to depth. The heat loss parameter can comprise one or more ofthe geothermal temperature, a deviation, dimensions of the tubulars thatare in the well, well head pressure, individual separator rates,downhole pressure, gas/liquid ratio, or the like. The autocorrelationcan comprise a cross-correlation of the distributed temperature sensingsignal with itself.

Once the temperature features are obtained, the temperature features canbe used with one or more first or wellbore event models to identify thepresence of the event at one or more locations. In some embodiments, theone or more first or wellbore event models can accept a plurality oftemperature features as inputs. In general, the temperature features arerepresentative of feature at a particular location (e.g., a depthresolution portion of the optical fiber along the length of the wellbore114 being monitored) along the length. The one or more first or wellboreevent models can comprise one or more models configured to accept thetemperature features as input(s) and provide an indication of whether ornot there is an event at the particular location along the length. Theoutput of the one or more first or wellbore event models can be in theform of a binary yes/no result, and/or a likelihood of an event (e.g., apercentage likelihood, etc.). Other outputs providing an indication ofan event are also possible. In some embodiments, the one or more firstor wellbore event models can comprise a machine learning model usingsupervised or unsupervised learning algorithms such as a multivariatemodel, neural network, or the like.

In some embodiments, the one or more first or wellbore event models cancomprise a multivariate model. A multivariate model allows for the useof a plurality of variables in a model to determine or predict anoutcome. A multivariate model can be developed using known data onevents along with features for those events to develop a relationshipbetween the features and the presence of the event at the locationswithin the available data. One or more multivariate models can bedeveloped using data, where each multivariate model uses a plurality offeatures as inputs to determine the likelihood of an event occurring atthe particular location along the length.

As noted above, in some embodiments, the one or more first or wellboreevent models can comprise one or more multivariate models that use oneor more features (e.g., temperature features, frequency domain features,other features derived from other types of sensors, etc.). Themultivariate model can use multivariate equations, and the multivariatemodel equations can use the features or combinations or transformationsthereof to determine when an event is present. The multivariate modelcan define a threshold, decision point, and/or decision boundary havingany type of shapes such as a point, line, surface, or envelope betweenthe presence and absence of the specific event. In some embodiments, themultivariate model can be in the form of a polynomial, though otherrepresentations are also possible. The model can include coefficientsthat can be calibrated based on known event data. While there can bevariability or uncertainty in the resulting values used in the model,the uncertainty can be taken into account in the output of the model.Once calibrated or tuned, the model can then be used with thecorresponding features to provide an output that is indicative of theoccurrence of an event.

The multivariate model is not limited to two dimensions (e.g., twofeatures or two variables representing transformed values from two ormore features), and rather can have any number of variables ordimensions in defining the threshold between the presence or absence ofthe event. When used, the detected values can be used in themultivariate model, and the calculated value can be compared to themodel values. The presence of the event can be indicated when thecalculated value is on one side of the threshold and the absence of theevent can be indicated when the calculated value is on the other side ofthe threshold. In some embodiments, the output of the multivariate modelcan be based on a value from the model relative to a normal distributionfor the model. Thus, the model can represent a distribution or envelopeand the resulting features can be used to define where the output of themodel lies along the distribution at the location along the length beingmonitored (e.g., along the length of the wellbore). Thus, eachmultivariate model can, in some embodiments, represent a specificdetermination between the presence or absence of an event at a specificlocation along the length. Different multivariate models, and thereforethresholds, can be used for different events, and each multivariatemodel can rely on different features or combinations or transformationsof features. Since the multivariate models define thresholds for thedetermination and/or identification of events, the multivariate modelsand the one or more first or wellbore event models using suchmultivariate models can be considered to be based event signatures foreach type of event.

In some embodiments, the one or more first or wellbore event models cancomprise a plurality of models. Each of the models can use one or moreof the features as inputs. The models can comprise any suitable modelthat can relate one or more features to an occurrence of an event (e.g.,a likelihood of the event, a binary yes/no output, etc.). The output ofeach model can then be combined to form a composite or combined output.The combined output can then be used to determine if an event hasoccurred, for example, by comparing the combined output with a thresholdvalue. The determination of the occurrence of an event can then be basedon the comparison of the combined output with the threshold value.

As an example, the one or more first or wellbore event models caninclude a plurality of multivariate models, each using a plurality offeatures as described above. The output of the multivariate models caninclude a percentage likelihood of the occurrence of an event at theparticular location at which each model is applied. The resulting outputvalues can then be used in a function such as a simple multiplication, aweighted average, a voting scheme, or the like to provide a combinedoutput. The resulting output can then be compared to a threshold todetermine if an event has occurred. For example, a combined outputindicating that there is greater than a fifty percent likelihood of anevent at the particular location can be taken as an indication that theevent has occurred at the location of interest.

In some embodiments, the one or more first or wellbore event models canalso comprise other types of models, including other machine learningmodels, first principles models, and/or physics based models. In someembodiments, a machine learning approach comprises a logistic regressionmodel. In some such embodiments, one or more features can be used todetermine if an event is present at one or more locations of interest.The machine learning approach can rely on a training data set that canbe obtained from a test set-up or obtained based on actual data fromknown events (e.g., from in-situ data as described herein in any of theaspects or embodiments). The one or more features in the training dataset can then be used to train the one or more first or wellbore eventmodels using machine learning, including any supervised or unsupervisedlearning approach. For example, the one or more first or wellbore eventmodels can include or consist of a neural network, a Bayesian network, adecision tree, a logistical regression model, a normalized logisticalregression model, or the like. In some embodiments, the one or morefirst or wellbore event models can comprise a model developed usingunsupervised learning techniques such a k-means clustering and the like.

In some embodiments, the one or more first or wellbore event models canbe developed and trained using a logistic regression model. As anexample for training of a model used to determine the presence orabsence of an event, the training of the model can begin with providingthe one or more temperature features to the logistic regression modelcorresponding to one or more reference data sets in which event(s) arepresent. Additional reference data sets can be provided in whichevent(s) are not present. The one or more features can be provided tothe logistic regression model, and a first multivariate model can bedetermined using the one or more features as inputs. The firstmultivariate model can define a relationship between a presence and anabsence of the events.

Once the one or more first or wellbore event models are trained, the oneor more first or wellbore event models can be used to determine thepresence or absence of an event at one or more locations along thelength of the wellbore 114, and the one or more events identified at 13can be utilized at 17 to identify corresponding data for training theone or more event models. The features determined for each locationalong the length can be used with the one or more first or wellboreevent models. The output of the one or more first or wellbore eventmodels can provide an indication of the presence of an event at eachlocation for which the temperature features are obtained. When theoutput indicates that an event has occurred at a given location, anoutput can be generated indicating the presence of the event. Theprocess can be repeated along the length to provide an event profile,which can comprise an indication of the events at one or more locationsalong the length being monitored. In some aspects, the event may beknown or induced, and the use of the first wellbore event models may notbe used to identify the event.

In some embodiments, the determination of the one or more events can bepresented as a profile along a length on an output device. The outputscan be presented in the form of an event profile depicted along an axiswith or without a schematic. The event profile can then be used tovisualize the event locations, which can allow for various processes tobe carried out. For example, for events comprising fluid flow, the fluidflow locations can be compared to the producing zones within acompletion to understand where fluid is entering, leaving, or flowingalong the wellbore. In some embodiments, fluid flow can be detected atlocations other than a producing zone, which may provide an indicationthat a remediation procedure is needed within the wellbore 114. Forexample, fluid flow during a shut-in period outside of a producing zonemay indicate a leak behind the casing.

The identification of the event at step 13 allows the second set ofmeasurements of the second signal to be obtained and associated orlabeled with the event. For example, DTS measurements and/or temperaturefeatures can be used to identify an event at a location in the wellbore.A second set of measurements such as acoustic measurements can then betaken and labeled as being associated with an identified event. Thelabeled data can then be used to train the one or more event models at17, as described in more detail below. Obtaining the second set ofmeasurements at step 15 can occur simultaneously with obtaining thefirst set of measurements at step 11. For example, both sets ofmeasurements can be detected at the same time. Once the event isidentified using the first set of measurements, the second set ofmeasurements can be stored with the event identification. Since someevents are relatively constant, obtaining the first set of measurementscan occur prior to or after obtaining the second set of measurements.For example, flow rate measurements from a PLT can be used to identify afluid flow of a specific phase at a first time. The PLT can then bemoved in the wellbore or removed altogether, and a second set ofmeasurements can be obtained, where the fluid flow can be assumed to bethe same at the time of the second set of measurement even though theyare not obtained simultaneously. The resulting event identification canthen be used to label the in-situ data for use in training the one ormore event models at step 17.

According to this disclosure, the one or more second event models can betrained using a labeled data set, obtained from field or in situ data(i.e., from event locations identified from the first set ofmeasurements of the first signal) that is labeled using otherinstrumentation to identify the presence and/or extent of an event. Insome embodiments, the one or more second event models can be furthertrained using a labeled data set, which can be obtained using a testapparatus such as a test flow set-up and/or field data that is labeledusing other instrumentation to identify the extent of an event. Usinglabeled data, the method of developing the one or more second eventmodels can include determining one or more frequency domain featuresfrom the acoustic signal for at least a portion of the data from thelabeled data. The one or more frequency domain features can be obtainedacross the portion of length where the event occurs, which can bedetermined using the first event model or models. The second event modelcan then be trained using the frequency domain features from the labeleddata and/or the tests. The training of the second event model can usemachine learning, including any supervised or unsupervised learningapproach. For example, the one or more second event models can includeor be a neural network, a Bayesian network, a decision tree, alogistical regression model, a normalized logistical regression model,k-means clustering or the like.

In some embodiments, the one or more second event models can bedeveloped and trained using a logistic regression model. As an examplefor training of a model used to determine the extent of an eventcomprising fluid flow (e.g., to determine the fluid flow rate), thetraining of the one or more second event models can begin with providingone or more frequency domain features to the logistic regression modelcorresponding to one or more event tests where known event extents havebeen measured. Similarly, one or more frequency domain features can beprovided to the logistic regression model corresponding to one or moretests where no event is present. A first multivariate model can bedetermined using the one or more frequency domain features as inputs.The first multivariate model can define a relationship between apresence and an absence of the event and/or event extent.

In the one or more second event models, the multivariate model equationscan use the frequency domain features or combinations or transformationsthereof to determine when a specific event or event extent (e.g., aspecific fluid flow rate or fluid flow rate for a fluid phase) ispresent. The multivariate model can define a threshold, decision point,and/or decision boundary having any type of shapes such as a point,line, surface, or envelope between the presence and absence of the eventor an event extent (e.g., the specific fluid flow rate or fluid flowrate for a phase). In some embodiments, the multivariate model can be inthe form of a polynomial, though other representations are alsopossible. When models such a neural networks are used, the thresholdscan be based on node thresholds within the model. As noted herein, themultivariate model is not limited to two dimensions (e.g., two frequencydomain features or two variables representing transformed values fromtwo or more frequency domain features), and rather can have any numberof variables or dimensions in defining the threshold between thepresence or absence of the event (e.g., fluid flow) and the specificevent extents (e.g., fluid flow rates for one or more fluids and/orfluid phases). Different multivariate models can be used for variousevents and/or event extents (e.g., flow rate for each fluid type and/orfluid flow phase), and each multivariate model can rely on differentfrequency domain features or combinations or transformations offrequency domain features.

Whether a test system or in situ sensors are used to obtain data on theevent extents (e.g., flow rates), collectively referred to as “referencedata”, one or more models can be developed for the determination of theevent extents (e.g., flow rates) using the reference data. The model(s)can be developed by determining one or more frequency domain featuresfrom the acoustic signal for at least a portion of the reference data.The training of the model(s) can use machine learning, including anysupervised or unsupervised learning approach. For example, one or moreof the model(s) can be a neural network, a Bayesian network, a decisiontree, a logistical regression model, a normalized logistical regressionmodel, k-means clustering, or the like.

The one or more frequency domain features used in the one or more secondevent models can include any frequency domain features noted hereinaboveas well as combinations and transformations thereof. For example, Insome embodiments, the one or more frequency domain features comprise aspectral centroid, a spectral spread, a spectral roll-off, a spectralskewness, an RMS band energy, a total RMS energy, a spectral flatness, aspectral slope, a spectral kurtosis, a spectral flux, a spectralautocorrelation function, combinations and/or transformations thereof,or any normalized variant thereof. In some embodiments, the one or morefrequency domain features comprise a normalized variant of the spectralspread (NVSS) and/or a normalized variant of the spectral centroid(NVSC).

The output of the (trained) one or more second event models can comprisean indication of the event location(s) and/or extent(s) (e.g., the flowrate(s) of one or more fluids and/or fluid phases). For example, forevents comprising fluid flow, the total liquid flow rate at one or moreevent locations can be determined from the one or more second eventmodels. The resulting output can, in aspects, be compared to the outputof the one or more first or wellbore event models to allow the event(e.g., fluid flow) location determination to be based both on the one ormore first or wellbore event models (e.g., using the temperaturefeatures) and the one or more second event models (e.g., using thefrequency domain features). In aspects, a final output can be a functionof both the output from the one or more first or wellbore event modelsand the one or more second event models. In some embodiments, theoutputs can be combined as a product, weighted product, ratio, or othermathematical combination. Other combinations can include voting schemes,thresholds, or the like to allow the outputs from both models to becombined. As an example, if the output from either model is zero, thenthe event identification at the location would also indicate that thereis no event at the location. In this example, one model can indicatethat an event is present, but the other model can indicate that no eventis present. The final result can indicate that no event is present. Whenboth models indicate that the event is present, the final combinedoutput can provide a positive indication of the event at the location.It is noted that the output of the one or more second event models canprovide one or more indications of event extents (e.g., a fluid flowrate of one or more fluids and/or fluid phases). While this output canbe distinct from the output of the one or more first or wellbore eventmodels, the two outputs can be combined to improve the accuracy of theevent location identification.

In aspects, a combined or hybrid approach to determining event extents(e.g., fluid flow rates) at the one or more locations at which an event(e.g., fluid flow) is identified is utilized. In these embodiments, theoutputs of the one or more first or wellbore event models and the one ormore (trained) second event models can be used together to help todetermine or confirm the presence and/or extent of an event (e.g., aflow rate of one or more fluids and/or fluid phases) along the lengthbeing monitored (e.g., within the wellbore 114). In some embodiments,the outputs of the two models can be combined to form a final eventpresence and/or event extent determination.

Subsequent to the training of the one or more event models at 17, theone or more second event models can use one or more frequency domainfeatures in one or more event models to validate the identified one ormore events and/or predict an extent of the event(s) (e.g., a quantityor flow rate of one or more fluids and/or fluid phases into the wellbore114, amount of leakage from a pipeline, etc.). For example, when theevent comprises fluid flow in a wellbore 114, the one or more secondevent models can be used to identify the fluid flow, to validate a flowlocation identified by the one or more first or wellbore event models,and/or predict the flow rates of one or more fluids including a gas, anaqueous liquid, a hydrocarbon liquid, or another fluid within thewellbore 114. In some embodiments, the one or more second event modelscan be utilized to predict the flow rate of a fluid phase such as a gasphase and/or a liquid phase (e.g., including a liquid aqueous phase anda hydrocarbon liquid phase).

In some embodiments, the frequency domain features can be used with oneor more second event models to predict a fluid flow rate, such as aliquid flowrate into the wellbore 114. The one or more second eventmodels can relate a fluid flow rate of one or more phases (e.g.,including a total liquid flow rate) to one or more frequency domainfeatures. In some embodiments, the trained one or more second eventmodels can accept one or more frequency domain features as inputs. Ingeneral, the frequency domain features are representative of feature ata particular location (e.g., a depth resolution portion of the opticalfiber along the length, e.g., the length of the wellbore) along thelength. The one or more second event models can comprise one or moremodels configured to accept the frequency domain features as input(s)and provide an indication of the presence and/or extent of the event(e.g., a fluid flow rate) at one or more locations within wellbore 114.When the event comprises fluid inflow, for example, the output of theone or more second event models can be, for example, in the form of aflow rate of one or more fluids and/or fluid phases. In someembodiments, the one or more second event models can comprise amultivariate model, a machine learning model using supervised orunsupervised learning algorithms, or the like.

In some embodiments, one or more second event models can be developedusing a machine learning approach. In some such embodiments, a singlefrequency domain feature (e.g., spectral flatness, RMS bin values, etc.)can be used to determine if the event is present at each location ofinterest. In some embodiments, the supervised learning approach can beused to determine a model of the event extent (e.g., flow rate of one ormore fluids and/or fluid phases, such as gas flow rate, a hydrocarbonflow rate, a water flow rate, a total gas phase flow rate, and/or atotal liquid phase (e.g., a liquid aqueous phase and a liquidhydrocarbon phase) flow rate).

In some aspects, the event identification and corresponding referencedata can be used to calibrate the one or more first event models. Inthis context, training the one or more second event models can include acalibration process. For example, the models or structure of the model(e.g., the type of model, identification of the model variables, etc.)can be known or pre-trained, and the event identification andcorresponding reference data can be used as a new training data set orused to supplement the original training data set to re-train orcalibrate the one or more second event models. This can allow one ormore parameters (e.g., coefficients, weightings, etc.) to be updated orcalibrated to provide a more accurate model. This process may be usefulto calibrate existing models for specific wellbores, formations, orfields to improve the event identifications in those locations.

Continuing the example above for a fluid flow events, the use of theevent identification and reference data can allow for a fluid flow eventmodel to be trained using the fluid flow event identification andreference data to be used as the input data. A fluid inflow model suchas a hydrocarbon inflow model, may be defined by one or more frequencydomain features and a relationship between the features. The eventidentification can be used to select the appropriate model (e.g., asdefined by the identification and relationship of the one or morefrequency domain features), and the reference data can be used to trainthe model to determine the model parameters (e.g., coefficients,weightings, etc.). This process can represent a calibration of the oneor more second event models rather than developing an entirely newmodel.

The in-situ identification of training data can also be used tocross-check and validate existing models. For example, the in-situidentified data can be used to train the one or more second event modelsas described herein. When an additional event is identified using thetrained one or more second models, the event identification can be usedto identify additional data using the first signals, which wouldcorrespond to the first set of measurements. The first set of models canbe trained to verify whether or not the newly trained model matches theoriginal model within a given threshold. When the models match, thesystem can provide an indication that the event is the only eventpresent. When the models do not match, it can be an indication thatanother, unidentified event is present within the data. Additionaltraining and event identification can then be used to identify theadditional event. The cross-checking and validation process can becarried out using subsequent data in time, at different depths along thewellbore, and/or across different wellbores.

Using the example above for fluid flow events, DTS data can be used toidentify an event during a fluid flow event such as a hydrocarbon flow.Corresponding DAS acoustic data can be obtained during the hydrocarbonflow event, and the resulting reference data can be used to train one ormore second event models for hydrocarbon flow using one or morefrequency domain features obtained from the DAS data. The resultinghydrocarbon flow event models using the one or more frequency domainfeatures can then be used alone or in combination with the DTS models toidentify a hydrocarbon flow event.

Continuing with the example, the DAS data can be used to identify ahydrocarbon flow event using the trained hydrocarbon flow models. When ahydrocarbon flow event is detected, additional data such as DTS data canbe obtained. The training process can then be repeated using the DTSdata to train a hydrocarbon flow model, and the resulting trained modelcan be compared to the original DTS model for hydrocarbon flow. If themodels match within a threshold (e.g., within a margin of error, etc.),then the models can be understood to detect hydrocarbon flow withreasonable certainty. However, if the models do not match, an additionalevent may be present. For example, the flow event as detected by thetrained hydrocarbon flow model using the one or more frequency domainfeatures may include both hydrocarbon flow and water flow. By trainingthe model using the identified DTS data, the model may not match theoriginal model due to the presence of the water in the flowing fluids.

When the models do not match within a threshold or margin of error, thevarious data can be used to identify one or more events and identify anyremaining noise or background signals. The remaining signals can then beattributed to a separate event that can be identified using othersignatures, models, or processes. For example, the produced fluids canbe observed on the surface to provide data indicating that water ispresent in addition to the hydrocarbon fluids. This information can thenbe used with the noise signals to identify additional data that can beused to train an additional one or more event models to capture theadditional events.

Even when the original model and the additional model match within amargin of error, the process can be used to improve both sets of models.In some embodiments, once one or more second event models are trainedusing the reference data, the one or more second event models can beused to identify one or more events. Additional data using a signal thatrepresents a different physical measurement, which can be the same asthe first signal used to train the one or more second event models, canbe obtained and labeled using the identification of the event. Theoriginal thresholds, signatures, and/or models can then be retrainedusing the new reference data and/or a set of reference data supplementedby the new reference data (e.g., the original training data set and thenew reference data combined to provide a larger training data set). Thisprocess can provide an improvement in the model output.

This process can be carried out at different locations along thewellbore, at different locations in different wellbores, and/or atdifferent times in the same or different locations in the wellbore or aseparate wellbore. This can allow for an improved reference data set(e.g., that is labeled with the identified events) that can be used totrain the one or more event models over time to provide improved resultsfor event identification.

FIG. 6 illustrates a flow chart for a method 500 of determining thepresence and/or extent of an event after training of the one or moreevent models. Subsequent to training the one or more event models, theone or more event models can be utilized alone or in conjunction withand/or the one or more wellbore event models or other data. For example,subsequent training of one or more event models with DAS data incombination with the location of one or more events identified via DTSdata, the one or more trained event models can be utilized alone or incombination with the one or more wellbore event models to identify atleast one additional event in the or another wellbore. In applications,DAS and DTS can be combined as described, for example, in PCT PatentApplication No. PCT/EP2020/051817, entitled, “Event CharacterizationUsing Hybrid DAS/DTS Measurements”, filed on Jan. 24, 2020, which isincorporated herein in its entirety. At step 502, temperature featurescan be determined using any of the processes and systems as describedherein. In some embodiments, a DTS system can be used to obtaindistributed temperature sensing signal along the length being monitored(e.g., along a length within the wellbore 114). The DTS system canprovide distributed temperature measurements along the length over time.A baseline temperature can be stored for the length as described hereinand used along with the temperature measurements to determine thetemperature features. The temperature features can include any of thosedescribed herein including a depth derivative of temperature withrespect to depth, a temperature excursion measurement, a baselinetemperature excursion, a peak-to-peak value, a fast Fourier transform, aLaplace transform, a wavelet transform, a derivative of temperature withrespect to length (e.g., depth), a heat loss parameter, anautocorrelation, a statistical measure of a variation with respect totime and/or distance, as detailed hereinabove, or a combination thereof.

At step 504, one or more frequency domain features can be obtained froman acoustic signal originating along the length being monitored (e.g.,within the wellbore 114). The frequency domain features can bedetermined using any of the processes and systems as described herein.In some embodiments, a DAS system can be used to obtain a distributedacoustic signal along the length of wellbore 114 being monitored. Theacoustic signals obtained from the DAS system can then be processed todetermine one or more frequency domain features as described herein. Insome embodiments, the frequency domain features can comprise at leastone of: a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, aspectral autocorrelation function, or any combination thereof, includingcombinations and modifications thereof.

The temperature features and/or the frequency domain features can thenbe used to determine a presence and/or extent of one or more events(e.g., a fluid inflow at one or more locations in a wellbore 114 and/ora fluid inflow rate thereof) at one or more locations along the lengthbeing monitored in step 506. The temperature features and/or thefrequency domain features can be used in several ways to determine thepresence and/or the extent of the one or more events along the lengthbeing monitored. In some embodiments, the temperature features can beused in the one or more first or wellbore event models to obtain anidentification of one or more locations along the length having theevent. Any of the models and methods of using the temperature featureswithin the models as described herein can be used to determine the oneor more event (e.g., fluid inflow) locations. As detailed hereinabove,the output of the one or more first or wellbore event models wasutilized (e.g., at step 13 of FIG. 1 ) to provide labeled training datafrom which the one or more event models was trained, after which, theone or more wellbore event models and/or the one or more event modelscan be utilized alone or in combination with the one or more wellboreevent models in the well or another well to provide an indication of oneor more locations along the length being monitored (e.g., along thelength of the wellbore) having at least one additional event in the oranother wellbore.

In aspects, frequency domain features can be used in the trained one ormore second event models to obtain an indication of the event extent(e.g., fluid inflow rate for one or more fluids and/or fluid phases) atthe one or more locations along the length of the wellbore 114. In someembodiments, the one or more second event models can be limited to beingexecuted at the one or more locations identified by the one or morefirst or wellbore event models. The one or more second event models canthen predict the event extent(s) (e.g., fluid inflow rates of one ormore fluids and/or fluid phases) at the one or more locations. The eventextent(s) (e.g., fluid inflow rates) can then be representative of theevent extents at the one or more locations along the length of thewellbore 144.

In some embodiments, subsequent to the training at 17, the output of theone or more first or wellbore event models and the one or more secondevent models can be combined to provide a combined output from the oneor more first or wellbore event models and the one or more second eventmodels to identify the at least one additional event at step 19. Theresulting combined output can then be used to determine an event extent(e.g., a fluid inflow rate) at the one or more locations along thelength of wellbore 114 being monitored as identified by the one or morefirst or wellbore event models. The combined output can be determined asa function of the output of the one or more first or wellbore eventmodels and the output of the one or more second event models. Anysuitable functions can be used to combine the outputs of the two models.This can include formulas, products, averages, and the like, each ofwhich can comprise one or more constants or weightings to provide thefinal output. The ability to determine the event extent(s) as a functionof the output of both models can allow for either model to override theoutput of the other model. For example, if the one or more first orwellbore event models indicate that a location along the length beingmonitored has an event, but the one or more second event models indicateno event, the resulting combined output may be considered to indicatethat there is no event at that location. Similarly, if the one or morefirst or wellbore event models indicate a non-zero but low likelihood ofan event at a location, the output can serve as a weighting to any eventextents determined by the one or more second event models. Thus, ahybrid model approach can be utilized to provide two separate ways toverify and determine the event extents along the length (e.g., fluidinflow rates into the wellbore 114). Alternatively, subsequent to thetraining of the one or more second event models, the one or more secondevent models are utilized alone to identify the at least one additionalevent within the wellbore 114 at 19.

The resulting output of the one or more event model(s) (and/or the oneor more wellbore event models) at 19 can be an indication of an event atone or more locations along the length. The event prediction can be forone or more events (e.g., one or more fluids (e.g., a gas, an aqueousliquid, a hydrocarbon liquid, etc.) and/or a fluid phase (e.g., a gasphase, a liquid phase, etc.)). The event extents can be used asindicated by the model in their form as output by the model. In someembodiments, the total event extents can be normalized across the one ormore locations having the event. This can allow for a determination of arelative proportion of the event at each of the identified locations.This can be useful for understanding where the contributions to an eventare occurring along the length, irrespective of the absolute eventextent along the length.

In some embodiments, the event extents can be refined by using anindependent measure of the event extent (e.g., fluid flow rate from thewellbore as measured at logging tool above the producing zones, awellhead, surface flow line, or the like). Thus, as depicted in FIG. 6 ,method 500 can further comprise optional step 508 of independentlymeasuring an event extent. For example, when the event comprises fluidinflow and the event extent comprises the fluid inflow rate, the fluidproduction rate can be measured by a standard fluid flowrate measurementtool that is not associated with the acoustic monitoring system or thetemperature monitoring system within the wellbore 114. For example, thefluid production rate can be measured with various flow meters. Thefluid production rate can comprise an indication of the fluid flow ratesof one or more fluids and/or one or more fluid phases. The resultingevent extent (e.g., fluid production rate) information can then becombined with the output of the models as described herein. In someembodiments, the resulting normalized event extents can be used with theactual event extents (e.g., production rates) to allocate the actualevent extent (e.g., production rates) across the one or more event(e.g., fluid) inflow locations along the length being monitored (e.g.,within the wellbore 114). Thus, method 500 of FIG. 6 can furthercomprise optional step 510 of allocating the event extent across the oneor more locations. As an example, for events comprising fluid inflow andevent extents comprising fluid inflow rates at one or more locations, ifthe model(s) indicate that thirty percent of a liquid phase inflow rateis occurring at a first location and seventy percent is occurring at asecond location, the actual production rate can be allocated so thatthirty percent of the produced liquid phase flowrate is attributed tothe first location and the remaining seventy percent of the liquid phaseflow rate is flowing into the wellbore at the second location. Theallocations can be made for one or more of the fluid inflow rates and/orfluid phase inflow rates, where the actual production rates for thefluids and/or fluid phases can be used with the corresponding modeloutputs for one or more fluids and/or fluid phases. The allocationprocess can allow for an improved accuracy for the determination offluid inflow rates at the one or more locations along the wellbore 114.

Also disclosed herein is a method of predicting wellbore sensor data.Description of such a method of predicting wellbore sensor data will nowbe made with reference to FIG. 7 , which is a flow diagram of a method20 of predicting wellbore sensor data according to some embodiments. Asdepicted in FIG. 7 , method 20 comprises: obtaining a first set ofmeasurements of a first signal within a wellbore 114 at 21; identifyingone or more events within the wellbore 114 using the first set ofmeasurements at 22; obtaining a second set of measurements of a secondsignal within the wellbore 114 at 23, wherein the first signal and thesecond signal represent different physical measurements; training one ormore event models using the second set of measurements and theidentification of the one or more events as inputs at 24; identifying,using the one or more event models, one or more additional events withinthe wellbore 114 at 25; using the one or more additional events with oneor more formation properties at 26; and predicting a third set ofmeasurements in response to combining the one or more additional eventswith the formation properties at 27, wherein the third set ofmeasurements represents a third signal that is different than the firstsignal and the second signal. Steps 21, 22, 23, 24, and 25 correspondwith and can be substantially as described hereinabove with reference tosteps 11, 13, 15, 17, and 19, respectively, of FIG. 1 .

In embodiments, a method of predicting wellbore sensor data according tothis disclosure comprises: training one or more event models using asecond set of measurements and an identification of one or more eventsas inputs, wherein a first set of measurements of a first signal areobtained within a wellbore 114, wherein one or more events within thewellbore 114 are identified using the first set of measurements, whereinthe second set of measurements of a second signal are obtained withinthe wellbore 114, and wherein the first signal and the second signalrepresent different physical measurements; identifying, using the one ormore event models, one or more additional events within the wellbore114; using the one or more additional events with one or more formationproperties; and predicting a third set of measurements in response tocombining the one or more additional events with the formationproperties, wherein the third set of measurements represents a thirdsignal that is different than the first signal and the second signal.

The method of predicting wellbore sensor data according to thisdisclosure can further comprise: identifying, using the one or moreevent models, one or more additional events within the wellbore 114.

As noted above, the first signal and the second signal representdifferent physical measurements, and the third set of measurementsrepresents a third signal that is different than the first signal andthe second signal. For example, without limitation, in aspects, thethird set of measurements can be predicted pressure measurements alongthe wellbore 114, or predicted flow measurements along the wellbore 114.As described hereinabove with regard to FIG. 1 , the first set ofmeasurements can comprise at least one of distributed temperature sensor(DTS) measurements, production logging tool (PLT) measurements, flowmeter measurements, or pressure sensor measurements, and/or the secondset of measurements can comprise acoustic measurements obtained withinthe wellbore 114. As detailed previously with reference to the method ofeven identification described with reference to FIG. 1 , the one or moreevents can comprise inflow events, leak events, sand ingress events, orany combination thereof, and/or the first set of measurements and thesecond set of measurements can be obtained simultaneously or atdifferent time intervals.

The method can further comprise creating labeled data using theidentified one or more events and the second set of measurements. Therock properties can comprise porosity, permeability, or the like,provided, for example, as porosity or permeability logs, respectively.

By way of example, in aspects, the first set of measurements comprisesDTS data and the second set of measurements comprises DAS data. Thelocal or reference DTS data (e.g., first set of measurements) can beutilized as detailed hereinabove along with the DAS measurements (e.g.,the second set of data) to train one or more event models. Once trained,the one or more trained event models can subsequently be utilized in thesame or another well to predict and/or validate data. For example, inaspects, the DAS/DTS data are utilized as detailed herein to generatesynthetic thermal profiles (e.g., predicted DTS data in anotherwellbore) and/or synthetic pressure data (e.g., DPS) data in the same oranother well. The synthetic or predicted data can be utilized to crosscheck data obtained via another means or sensor. For example, predictedor synthetic flow logs from the one or more trained event models can beutilized to cross validate PLT data obtained in situ. Alternatively oradditionally, DPS data predicted from the trained event models incombination with the rock properties can be utilized to cross checkpressure measurements from one or more in situ pressure sensors. One ofskill in the art and with the help of this disclosure will understandthat the herein disclosed system and method can be utilized to predict avariety of wellbore sensor data, which can be utilized in many ways toenhance wellbore management, planning, and production.

Any of the systems and methods disclosed herein can be carried out on acomputer or other device comprising a processor (e.g., a desktopcomputer, a laptop computer, a tablet, a server, a smartphone, or somecombination thereof), such as the acquisition device 160 of FIG. 3 .FIG. 8 illustrates a computer system 680 suitable for implementing oneor more embodiments disclosed herein such as the acquisition device orany portion thereof. The computer system 680 includes a processor 682(which may be referred to as a central processor unit or CPU) that is incommunication with memory devices including secondary storage 684, readonly memory (ROM) 686, random access memory (RAM) 688, input/output(I/O) devices 690, and network connectivity devices 692. The processor682 may be implemented as one or more CPU chips.

It is understood that by programming and/or loading executableinstructions onto the computer system 680, at least one of the CPU 682,the RAM 688, and the ROM 686 are changed, transforming the computersystem 680 in part into a particular machine or apparatus having thenovel functionality taught by the present disclosure. It is fundamentalto the electrical engineering and software engineering arts thatfunctionality that can be implemented by loading executable softwareinto a computer can be converted to a hardware implementation bywell-known design rules. Decisions between implementing a concept insoftware versus hardware typically hinge on considerations of stabilityof the design and numbers of units to be produced rather than any issuesinvolved in translating from the software domain to the hardware domain.Generally, a design that is still subject to frequent change may bepreferred to be implemented in software, because re-spinning a hardwareimplementation is more expensive than re-spinning a software design.Generally, a design that is stable that will be produced in large volumemay be preferred to be implemented in hardware, for example in anapplication specific integrated circuit (ASIC), because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

Additionally, after the system 680 is turned on or booted, the CPU 682may execute a computer program or application. For example, the CPU 682may execute software or firmware stored in the ROM 686 or stored in theRAM 688. In some cases, on boot and/or when the application isinitiated, the CPU 682 may copy the application or portions of theapplication from the secondary storage 684 to the RAM 688 or to memoryspace within the CPU 682 itself, and the CPU 682 may then executeinstructions of which the application is comprised. In some cases, theCPU 682 may copy the application or portions of the application frommemory accessed via the network connectivity devices 692 or via the I/Odevices 690 to the RAM 688 or to memory space within the CPU 682, andthe CPU 682 may then execute instructions of which the application iscomprised. During execution, an application may load instructions intothe CPU 682, for example load some of the instructions of theapplication into a cache of the CPU 682. In some contexts, anapplication that is executed may be said to configure the CPU 682 to dosomething, e.g., to configure the CPU 682 to perform the function orfunctions promoted by the subject application. When the CPU 682 isconfigured in this way by the application, the CPU 682 becomes aspecific purpose computer or a specific purpose machine.

The secondary storage 684 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 688 is not large enough tohold all working data. Secondary storage 684 may be used to storeprograms which are loaded into RAM 688 when such programs are selectedfor execution. The ROM 686 is used to store instructions and perhapsdata which are read during program execution. ROM 686 is a non-volatilememory device which typically has a small memory capacity relative tothe larger memory capacity of secondary storage 684. The RAM 688 is usedto store volatile data and perhaps to store instructions. Access to bothROM 686 and RAM 688 is typically faster than to secondary storage 684.The secondary storage 684, the RAM 688, and/or the ROM 686 may bereferred to in some contexts as computer readable storage media and/ornon-transitory computer readable media.

I/O devices 690 may include printers, video monitors, electronicdisplays (e.g., liquid crystal displays (LCDs), plasma displays, organiclight emitting diode displays (OLED), touch sensitive displays, etc.),keyboards, keypads, switches, dials, mice, track balls, voicerecognizers, card readers, paper tape readers, or other well-known inputdevices.

The network connectivity devices 692 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fiber distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 692 may enable the processor 682 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 682 mightreceive information from the network, or might output information to thenetwork (e.g., to an event database) in the course of performing theabove-described method steps. Such information, which is oftenrepresented as a sequence of instructions to be executed using processor682, may be received from and outputted to the network, for example, inthe form of a computer data signal embodied in a carrier wave.

Such information, which may include data or instructions to be executedusing processor 682 for example, may be received from and outputted tothe network, for example, in the form of a computer data baseband signalor signal embodied in a carrier wave. The baseband signal or signalembedded in the carrier wave, or other types of signals currently usedor hereafter developed, may be generated according to several knownmethods. The baseband signal and/or signal embedded in the carrier wavemay be referred to in some contexts as a transitory signal.

The processor 682 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 684), flash drive, ROM 686, RAM 688, or the network connectivitydevices 692. While only one processor 682 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors. Instructions,codes, computer programs, scripts, and/or data that may be accessed fromthe secondary storage 684, for example, hard drives, floppy disks,optical disks, and/or other device, the ROM 686, and/or the RAM 688 maybe referred to in some contexts as non-transitory instructions and/ornon-transitory information.

In an embodiment, the computer system 680 may comprise two or morecomputers in communication with each other that collaborate to perform atask. For example, but not by way of limitation, an application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of the instructions of the application. Alternatively, thedata processed by the application may be partitioned in such a way as topermit concurrent and/or parallel processing of different portions of adata set by the two or more computers. In an embodiment, virtualizationsoftware may be employed by the computer system 680 to provide thefunctionality of a number of servers that is not directly bound to thenumber of computers in the computer system 680. For example,virtualization software may provide twenty virtual servers on fourphysical computers. In an embodiment, the functionality disclosed abovemay be provided by executing the application and/or applications in acloud computing environment. Cloud computing may comprise providingcomputing services via a network connection using dynamically scalablecomputing resources. Cloud computing may be supported, at least in part,by virtualization software. A cloud computing environment may beestablished by an enterprise and/or may be hired on an as-needed basisfrom a third party provider. Some cloud computing environments maycomprise cloud computing resources owned and operated by the enterpriseas well as cloud computing resources hired and/or leased from a thirdparty provider.

In an embodiment, some or all of the functionality disclosed above maybe provided as a computer program product. The computer program productmay comprise one or more computer readable storage medium havingcomputer usable program code embodied therein to implement thefunctionality disclosed above. The computer program product may comprisedata structures, executable instructions, and other computer usableprogram code. The computer program product may be embodied in removablecomputer storage media and/or non-removable computer storage media. Theremovable computer readable storage medium may comprise, withoutlimitation, a paper tape, a magnetic tape, magnetic disk, an opticaldisk, a solid state memory chip, for example analog magnetic tape,compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,digital cards, multimedia cards, and others. The computer programproduct may be suitable for loading, by the computer system 680, atleast portions of the contents of the computer program product to thesecondary storage 684, to the ROM 686, to the RAM 688, and/or to othernon-volatile memory and volatile memory of the computer system 680. Theprocessor 682 may process the executable instructions and/or datastructures in part by directly accessing the computer program product,for example by reading from a CD-ROM disk inserted into a disk driveperipheral of the computer system 680. Alternatively, the processor 682may process the executable instructions and/or data structures byremotely accessing the computer program product, for example bydownloading the executable instructions and/or data structures from aremote server through the network connectivity devices 692. The computerprogram product may comprise instructions that promote the loadingand/or copying of data, data structures, files, and/or executableinstructions to the secondary storage 684, to the ROM 686, to the RAM688, and/or to other non-volatile memory and volatile memory of thecomputer system 680.

In some contexts, the secondary storage 684, the ROM 686, and the RAM688 may be referred to as a non-transitory computer readable medium or acomputer readable storage media. A dynamic RAM embodiment of the RAM688, likewise, may be referred to as a non-transitory computer readablemedium in that while the dynamic RAM receives electrical power and isoperated in accordance with its design, for example during a period oftime during which the computer system 680 is turned on and operational,the dynamic RAM stores information that is written to it. Similarly, theprocessor 682 may comprise an internal RAM, an internal ROM, a cachememory, and/or other internal non-transitory storage blocks, sections,or components that may be referred to in some contexts as non-transitorycomputer readable media or computer readable storage media.

Also disclosed herein is a system for identifying events within awellbore 114. The system comprises: a memory (e.g., RAM 688, ROM 686);an identification program stored in the memory; and a processor 682,wherein the identification program, when executed on the processor 682,configures the process to: receive a first set of measurements of afirst signal within a wellbore 114; identify one or more events withinthe wellbore 114 using the first set of measurements; receive a secondset of measurements of a second signal within the wellbore 114, whereinthe first signal and the second signal represent different physicalmeasurements; train one or more event models using the second set ofmeasurements and the identification of the one or more events as inputs;and use the one or more event models to identify at least one additionalevent within the wellbore 114.

As described hereinabove, in embodiments, the one or more eventscomprise inflow events, leak events, sand ingress events, or anycombination thereof. The second set of measurements can compriseacoustic measurements from the wellbore 114, and/or the first set ofmeasurements can be received from at least one of distributedtemperature sensor (DTS) measurements, production logging tool (PLT)measurements, flow meter measurements, or pressure sensor measurements.The first set of measurements and the second set of measurements can befrom a same time interval, or from different time intervals.

The processor 682 can be further configured to: create labeled datausing the identified one or more events and the second set ofmeasurements. In aspects, the processor 682 is further configured to:use the first set of measurements with one or more wellbore eventmodels; and identify the one or more events with the one or morewellbore event models. In some such embodiments, the processor 682 canbe further configured to: monitor the first signal within the wellbore114; monitor the second signal within the wellbore 114; use the firstsignal in the one or more wellbore event models; use the second signalin the one or more event models; and detect the one or more events basedon outputs of both the one or more wellbore event models and the one ormore event models.

Also disclosed herein is a system for predicting wellbore sensor data.The system comprises: a memory (e.g., RAM 688, ROM 686); a predictionprogram stored in the memory; and a processor 682, wherein theprediction program, when executed on the processor 682, configures theprocess to: receive a first set of measurements of a first signal,wherein the first set of measurements originate from within a wellbore114; identify one or more events within the wellbore 114 using the firstset of measurements; receive a second set of measurements of a secondsignal, wherein the second set of measurements originate from within thewellbore 114, wherein the first signal and the second signal representdifferent physical measurements; train one or more event models usingthe second set of measurements and the identification of the one or moreevents as inputs; identify, using the one or more event models, one ormore additional events within the wellbore 114; use the one or moreadditional events with one or more formation properties; and determine athird set of measurements in response to combining the one or moreadditional events with the formation properties, wherein the third setof measurements represent predicted physical parameters within thewellbore 114, wherein the third set of measurements represents a thirdsignal that is different than the first signal and the second signal.

In aspects, the prediction program is further configured to: identify,using the one or more event models, one or more additional events withinthe wellbore 114. As noted hereinabove with reference to FIG. 7 , thethird set of measurements can be, for example, predicted pressuremeasurements along the wellbore 114, and/or predicted flow measurementsalong the wellbore 114. The second set of measurements can compriseacoustic measurements obtained within the wellbore 114. The one or moreevents can comprise inflow events, leak events, sand ingress events, orany combination thereof. The first set of measurements can comprise atleast one of distributed temperature sensor (DTS) measurements,production logging tool (PLT) measurements, flow meter measurements, orpressure sensor measurements. The first set of measurements and thesecond set of measurements can be obtained simultaneously, or atdifferent time intervals.

The prediction program can be further configured to: create labeled datausing the identified one or more events and the second set ofmeasurements.

As detailed hereinabove, a first set of measurements from a wellbore 114can be utilized to train one or more event models operable with a secondset of measurements from a wellbore 114. The first set of measurementsand the second set of measurements can be obtained from the samewellbore 114, or another same or similar wellbore 114 (e.g., having asame completion type and/or in a same or similar formation). Utilizinglocal data as reference for training the one or more event models cansimplify the use of the one or more event models and subsequently (i.e.,after training the one or more event models), the trained one or moreevent models can be utilized alone or in conjunction with the first setof measurements (e.g., with one or more wellbore event models therefor)to identify at least one additional event in the or another wellbore114. In aspects, the trained one or more event models can be utilized inconjunction with the first set of measurements (e.g., with one or morewellbore event models therefor) to provide additional information beyondinformation either the one or more event models or the one or morewellbore event models can provide independently, and/or to providevalidation of the outputs from the one or more event models and/or theone or more wellbore event models. For example, when the first set ofmeasurements comprises DTS data and the second set of measurementscomprises DAS data, the one or more event models can be trained usingthe second set of measurements and the identification of the one or moreevents provided by the first set of measurements, and subsequently, theone or more trained event models can be utilized to determine thepresence or absence of an influx of liquid along with influx of gaswhere the one or more wellbore event models (e.g., the DTS data)indicate influx of fluid, but cannot specify liquid and/or gas. In thismanner, for example, influx of liquid can be decoupled from influx ofgas. The system and method of identifying events in a wellbore asdisclosed herein can thus be utilized to provide more information thancan typically be provided by the one or more wellbore event modelsand/or the one or more event models alone, and/or can be utilized tobuild confidence in the outputs thereof.

Furthermore, the system and method of wellbore sensor data allows thefirst set of measurements and the second set of measurements to beutilized to predict a third set of measurements. For example, inaspects, the first set of measurements comprises DTS data and the secondset of measurements comprises DAS data, and the DAS/DTS data areutilized as detailed herein to generate synthetic thermal profiles(e.g., predicted DTS data in another wellbore) and/or synthetic pressuredata (e.g., DPS) data. For example, DTS data can be utilized to trainone or more event models that utilize DAS measurements, and the trainedmodel subsequently utilized in the or another well to predict and/orvalidate DPS data. In aspects, the synthetic or predicted data can beutilized to cross check data. For example, in aspects, predicted orsynthetic pressure data can be utilized to cross validate obtained PLTdata.

The herein disclosed systems and methods can be utilized within a welland/or across wells and/or fields to determine where to place additionalwells and/or to optimize production from the well(s).

Having described various systems and methods, certain aspects caninclude, but are not limited to:

In a first aspect, a method of identifying events within a wellborecomprises: identifying one or more events within the wellbore; obtaininga first set of measurements comprising a first signal within thewellbore associated with the identified one or more event; training oneor more event models using the second set of measurements and theidentification of the one or more events as inputs; and using the one ormore event models to identify at least one additional event.

A second aspect can include the method of the first aspect, furthercomprising: obtaining a second set of measurements comprising a secondsignal within a wellbore, wherein identifying the one or more eventswithin the wellbore comprises identifying the one or more events withinthe wellbore using the second set of measurements, and wherein the firstsignal and the second signal represent different physical measurements.

A third aspect can include the method of the first or second aspect,wherein identifying the one or more events within the wellbore comprisesusing an identity of the one or more events based on a known event orinduced event within the wellbore.

A fourth aspect can include the method of the first or second aspect,wherein the first set of measurements comprises acoustic measurementsobtained within the wellbore.

A fifth aspect can include the method of any one of the first to fourthaspects, wherein the one or more events comprise a fluid inflow event a,a fluid outflow event, a fluid flow event within the wellbore, a fluidinjection event, a fluid phase flow, a mixed phase flow, a leak event, awell integrity event, an, annular fluid flow event, an overburden event,a fluid induced hydraulic fracture event, sand detection event, or anycombination thereof.

A sixth aspect can include the method of any one of the second to fifthaspects, wherein the second set of measurements comprise at least one ofan acoustic sensor measurement, a temperature sensor measurement, a flowsensor measurement, a pressure sensor measurement, a strain sensormeasurement, a position sensor measurement, a current meter measurement,a level sensor measurement, a phase sensor measurement, a compositionsensor measurement, an optical sensor measurement, an image sensormeasurement, or any combination thereof.

A seventh aspect can include the method of any one of the first to sixthaspects, further comprising: creating labeled data using the identifiedone or more events and the first set of measurements.

An eighth aspect can include the method of any one of the first toseventh aspects, wherein the first set of measurements and the secondset of measurements are obtained simultaneously.

A ninth aspect can include the method of any one of the first to eighthaspects, wherein the first set of measurements and the second set ofmeasurements are obtained at different time intervals.

A tenth aspect can include the method of any one of the second to ninthaspects, wherein identifying the one or more events comprises: using thesecond set of measurements with one or more wellbore event models; andidentifying the one or more events with the one or more wellbore eventmodels.

An eleventh aspect can include the method of the tenth aspect, furthercomprising: monitoring the first signal within the wellbore; monitoringthe second signal within the wellbore; using the second signal in theone or more wellbore event models; using the first signal in the one ormore event models; and detecting the at least one additional event basedon outputs of both the one or more wellbore event models and the one ormore event models.

A twelfth aspect can include the method of any one of the first toeleventh aspects, wherein the one or more event models are one or morepre-trained event models, and wherein training the one or more eventmodels using the first set of measurements and the identification of theone or more events as inputs comprises: calibrating the one or morepre-trained event models using the first set of measurements and theidentification of the one or more events as inputs; and updating atleast one parameter of the one or more pre-trained event models inresponse to the calibrating.

A thirteenth aspect can include the method of any one of the first totwelfth aspects, further comprising: obtaining a third set ofmeasurements comprising a third signal within a wellbore, wherein thethird signal and the first signal represent different physicalmeasurements, and wherein the third set of measurements represent the atleast one additional event; and training one or more additional eventmodels using the third set of measurements and the identification of theat least one addition event as inputs.

A fourteenth aspect can include the method of the thirteenth aspect,wherein identifying the one or more events within the wellbore using thefirst set of measurements comprises: using the one or more additionalevent models to identify the one or more events within the wellbore, andwherein training the one or more additional event models using the thirdset of measurements and the identification of the at least oneadditional event as inputs comprises: retaining the one or moreadditional event models using the third set of measurements and theidentification of the at least one additional event as inputs.

In a fifteenth aspect, a system for identifying events within a wellborecomprises: a memory; an identification program stored in the memory; anda processor, wherein the identification program, when executed on theprocessor, configures the process to: identify one or more events withinthe wellbore; receive a first set of measurements of a first signalwithin the wellbore; train one or more event models using the first setof measurements and the identification of the one or more events asinputs; and use the one or more event models to identify at least oneadditional event.

A sixteenth aspect can include the system of the fifteenth aspect,wherein the identification program further configures the processor to:receive a second set of measurements comprising a second signal, whereinthe identification of the one or more events within the wellborecomprises an identification of the one or more events within thewellbore based on the second set of measurements, and wherein the firstsignal and the second signal represent different physical measurements.

A seventeenth aspect can include the system of the fifteenth orsixteenth aspect, wherein the identification of the one or more eventswithin the wellbore comprises receiving an identity of the one or moreevents based on a known event or induced event within the wellbore.

An eighteenth aspect can include the system of any one of the fifteenthto seventeenth aspects, wherein the first set of measurements compriseacoustic measurements from the wellbore.

A nineteenth aspect can include the system of any one of the fifteenthto eighteenth aspects, wherein the one or more events comprise a fluidinflow event a, a fluid outflow event, a fluid flow event within thewellbore, a fluid injection event, a fluid phase flow, a mixed phaseflow, a leak event, a well integrity event, an, annular fluid flowevent, an overburden event, a fluid induced hydraulic fracture event,sand detection event, or any combination thereof.

A twentieth aspect can include the system of any one of the fifteenth tonineteenth aspects, wherein the second set of measurements are receivedfrom at least one of an acoustic sensor, a temperature sensor, a flowsensor, a pressure sensor, a strain sensor, a position sensor, a currentmeter, a level sensor, a phase sensor, a composition sensor, an opticalsensor, an image sensor, or any combination thereof.

A twenty first aspect can include the system of any one of the fifteenthto twentieth aspects, wherein the processor is further configured to:create labeled data using the identified one or more events and thefirst set of measurements.

A twenty second aspect can include the system of any one of thefifteenth to twenty first aspects, wherein the first set of measurementsand the second set of measurements are from a same time interval.

A twenty third aspect can include the system of any one of the sixteenthto twenty first aspects, wherein the first set of measurements and thesecond set of measurements are from different time intervals.

A twenty fourth aspect can include the system of any one of thesixteenth to twenty third aspects, wherein the processor is furtherconfigured to: use the second set of measurements with one or morewellbore event models; and identify the one or more events with the oneor more wellbore event models.

A twenty fifth aspect can include the system of the twenty fourthaspect, wherein the processor is further configured to: monitor thefirst signal within the wellbore; monitor the second signal within thewellbore; use the second signal in the one or more wellbore eventmodels; use the first signal in the one or more event models; and detectthe one or more events based on outputs of both the one or more wellboreevent models and the one or more event models.

A twenty sixth aspect can include the system of any one of the fifteenthto twenty fifth aspects, wherein the one or more event models are one ormore pre-trained event models, and wherein the processor is furtherconfigured to: calibrate the one or more pre-trained event models usingthe first set of measurements and the identification of the one or moreevents as inputs; and update at least one parameter of the one or morepre-trained event models in response to the calibrating.

In a twenty seventh aspect, a method of identifying events within awellbore comprises: obtaining a first set of measurements of a firstsignal within a wellbore; identifying one or more events within thewellbore using the first set of measurements, wherein the one or moreevents comprise a gas phase inflow, a liquid phase inflow, or sandingress into the wellbore; obtaining an acoustic data set from withinthe wellbore, wherein the first signal is not an acoustic signal;training one or more fluid inflow models using the acoustic data set andthe identification of the one or more events as inputs; and using thetrained one or more fluid inflow models to identify at least oneadditional fluid inflow event.

A twenty eighth aspect can include the method of the twenty seventhaspect, wherein the first set of measurements comprises distributedtemperature sensor measurements.

A twenty ninth aspect can include the method of the twenty seventh ortwenty eighth aspect, wherein the first set of measurements compriseproduction volumetric information.

A thirtieth aspect can include the method of any one of the twentyseventh to twenty ninth aspects, wherein identifying the one or moreevents within the wellbore comprises: identifying a first locationhaving a first event of the one or more events; and identifying thefirst event at the first location using one or more wellbore eventmodels.

A thirty first aspect can include the method of the thirtieth aspect,wherein training the one or more fluid inflow models comprises:obtaining acoustic data for the first location from the acoustic dataset; and training the one or more fluid inflow models using the acousticdata for the first location and the identification of the first event atthe first location.

A thirty second aspect can include the method of the thirty firstaspect, wherein using the trained one or more fluid inflow models toidentify the at least one additional fluid inflow event within thewellbore comprises using the one or more trained fluid inflow models toidentify the at least one additional fluid inflow event along the lengthof the wellbore.

The embodiments disclosed herein have included systems and methods foridentifying events and for predicting sensor data within a subterraneanwellbore, or a plurality of such wellbores. Thus, through use of thesystems and methods described herein, one may more effectively enhancethe economic production therefrom.

While exemplary embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thescope or teachings herein. The embodiments described herein areexemplary only and are not limiting. Many variations and modificationsof the systems, apparatus, and processes described herein are possibleand are within the scope of the disclosure. Accordingly, the scope ofprotection is not limited to the embodiments described herein, but isonly limited by the claims that follow, the scope of which shall includeall equivalents of the subject matter of the claims. Unless expresslystated otherwise, the steps in a method claim may be performed in anyorder. The recitation of identifiers such as (a), (b), (c) or (1), (2),(3) before steps in a method claim are not intended to and do notspecify a particular order to the steps, but rather are used to simplifysubsequent reference to such steps.

What is claimed is:
 1. A method of identifying events within a wellbore,the method comprising: obtaining a first set of measurements from afirst sensor disposed in the wellbore; obtaining a second set ofmeasurements comprising a second signal from a second sensor;identifying one or more events within the wellbore using the second setof measurements as inputs to one or more first event models, wherein thefirst set of measurements comprises a first signal within the wellboreassociated with the identified one or more event, wherein the firstsignal and the second signal represent different physical measurementsof different physical signals for the same one or more events; forming alabeled data set using the first set of measurements and theidentification of the one or more events; training one or more secondevent models using the labeled data set, wherein the one or more secondevent models are configured to identify the same one or more events asthe one or more first event models based on the different physicalsignals; and using the one or more second event models to identify atleast one additional occurrence and location of the one or more eventswithin the wellbore.
 2. The method of claim 1, wherein the second signalis obtained within the wellbore.
 3. The method of claim 1, whereinidentifying the one or more events within the wellbore further comprisesusing an identity of the one or more events based on a known event or aninduced event within the wellbore.
 4. The method of claim 1, wherein thefirst set of measurements comprises acoustic measurements obtainedwithin the wellbore.
 5. The method of claim 1, wherein the one or moreevents comprise a fluid inflow event, a fluid outflow event, a fluidflow event within the wellbore, a fluid injection event, a fluid phaseflow, a mixed phase flow, a leak event, a well integrity event, an,annular fluid flow event, an overburden event, a fluid induced hydraulicfracture event, sand detection event, or any combination thereof.
 6. Themethod of claim 1, wherein the second set of measurements comprise atleast one of an acoustic sensor measurement, a temperature sensormeasurement, a flow sensor measurement, a pressure sensor measurement, astrain sensor measurement, a position sensor measurement, a currentmeter measurement, a level sensor measurement, a phase sensormeasurement, a composition sensor measurement, an optical sensormeasurement, an image sensor measurement, or any combination thereof. 7.The method of claim 1, wherein the first set of measurements and thesecond set of measurements are obtained simultaneously.
 8. The method ofclaim 1, wherein the first set of measurements and the second set ofmeasurements are obtained at different time intervals.
 9. The method ofclaim 1, further comprising: monitoring the first signal within thewellbore; monitoring the second signal within the wellbore; using thesecond signal in the one or more first event models; using the firstsignal in the one or more second event models; and detecting the atleast one additional event based on outputs of both the one or morefirst event models and the one or more second event models.
 10. Themethod of claim 1, wherein the one or more second event models are oneor more pre-trained event models, and wherein training the one or moresecond event models using the labeled data comprises: calibrating theone or more pre-trained event models using the first set of measurementsand the identification of the one or more events as inputs; and updatingat least one parameter of the one or more pre-trained event models inresponse to the calibrating.
 11. The method of claim 1, furthercomprising: obtaining a third set of measurements comprising a thirdsignal within a wellbore, wherein the third signal and the first signalrepresent different physical measurements of different physical signals,and wherein the third set of measurements represent the at least oneadditional event; and training one or more additional event models usingthe third set of measurements and the identification of the at least oneaddition event as inputs.
 12. The method of claim 11, whereinidentifying the one or more events within the wellbore using the firstset of measurements comprises: using the one or more additional eventmodels to identify the one or more events within the wellbore, andwherein training the one or more additional event models using the thirdset of measurements and the identification of the at least oneadditional event as inputs comprises: retaining the one or moreadditional event models using the third set of measurements and theidentification of the at least one additional event as inputs.
 13. Asystem for identifying events within a wellbore, the system comprising:a memory; an identification program stored in the memory; and aprocessor, wherein the identification program, when executed on theprocessor, configures the process to: receive, from a first sensordisposed in the wellbore, a first set of measurements of a first signalwithin the wellbore; receive, from a second sensor, a second set ofmeasurements comprising a second signal; identify one or more eventswithin the wellbore using the second set of measurements as inputs toone or more first event models, wherein the first signal is associatedwith the identified one or more event, and wherein the first signal andthe second signal represent different physical measurements of differentphysical signals for the same one or more events; form a labeled dataset using the first set of measurements and the identification of theone or more events; train one or more second event models using thelabeled data set wherein the one or more second event models areconfigured to identify the same one or more events as the one or morefirst event models based on the different physical signals; and use theone or more second event models to identify at least one additionaloccurrence and location of the one or more events within the wellbore.14. The system of claim 13, wherein the identification of the one ormore events within the wellbore further comprises receiving an identityof the one or more events based on a known event or induced event withinthe wellbore.
 15. The system of claim 13, wherein the first set ofmeasurements comprise acoustic measurements from the wellbore.
 16. Thesystem of claim 13, wherein the one or more events comprise a fluidinflow event, a fluid outflow event, a fluid flow event within thewellbore, a fluid injection event, a fluid phase flow, a mixed phaseflow, a leak event, a well integrity event, an, annular fluid flowevent, an overburden event, a fluid induced hydraulic fracture event,sand detection event, or any combination thereof.
 17. The system ofclaim 13, wherein the second set of measurements are received from atleast one of an acoustic sensor, a temperature sensor, a flow sensor, apressure sensor, a strain sensor, a position sensor, a current meter, alevel sensor, a phase sensor, a composition sensor, an optical sensor,an image sensor, or any combination thereof.
 18. The system of claim 13,wherein the first set of measurements and the second set of measurementsare from a same time interval.
 19. The system of claim 13, wherein thefirst set of measurements and the second set of measurements are fromdifferent time intervals.
 20. The system of claim 13, wherein theprocessor is further configured to: monitor the first signal within thewellbore; monitor the second signal within the wellbore; use the secondsignal in the one or more first event models; use the first signal inthe one or more second event models; and detect the one or more eventsbased on outputs of both the one or more first event models and the oneor more second event models.
 21. The system of claim 13, wherein the oneor more event models are one or more pre-trained event models, andwherein the processor is further configured to: calibrate the one ormore pre-trained event models using the first set of measurements andthe identification of the one or more events as inputs; and update atleast one parameter of the one or more pre-trained event models inresponse to the calibrating.
 22. A method of identifying events within awellbore, the method comprising: identifying one or more events withinthe wellbore; obtaining a first set of measurements from a sensordisposed in the wellbore, wherein the first set of measurementscomprises a first signal from within the wellbore caused by theidentified one or more event, wherein the first signal represents afirst physical measurements at a location of the one or more eventsobtained during an occurrence of the one or more events; forming alabeled data set using the first set of measurements and theidentification of the one or more events; training one or more eventmodels using the labeled data set; and using the one or more eventmodels to identify at least one additional occurrence and location ofthe one or more events within the wellbore.
 23. The method of claim 22,further comprising: obtaining a second set of measurements comprising asecond signal within the wellbore, wherein identifying the one or moreevents within the wellbore comprises identifying the one or more eventswithin the wellbore using the second set of measurements, and whereinthe first signal and the second signal represent different physicalmeasurements of different physical signals.
 24. The method of claim 22,wherein identifying the one or more events within the wellbore comprisesusing an identity of the one or more events based on a known event or aninduced event within the wellbore.
 25. The method of claim 22, whereinthe one or more events comprise a gas phase inflow, a liquid phaseinflow, or sand ingress into the wellbore.